├── .gitignore ├── Cover.png ├── errata.pdf ├── extras ├── sopa_seca.jpeg ├── SopaSeca.md └── concrete.csv ├── code ├── Chp1 │ ├── B11197_01_01.png │ ├── B11197_01_02.png │ ├── B11197_01_03.png │ ├── B11197_01_04.png │ ├── B11197_01_05.png │ ├── B11197_01_06.png │ ├── B11197_01_07.png │ ├── B11197_01_08.png │ └── beta_binomial.odg ├── Chp2 │ ├── B11197_02_01.png │ ├── B11197_02_02.png │ ├── B11197_02_03.png │ ├── B11197_02_04.png │ ├── B11197_02_05.png │ ├── B11197_02_06.png │ ├── B11197_02_07.png │ ├── B11197_02_08.odg │ ├── B11197_02_08.png │ ├── B11197_02_09.png │ ├── B11197_02_10.png │ ├── B11197_02_11.png │ ├── B11197_02_12.png │ ├── B11197_02_13.odg │ ├── B11197_02_13.png │ ├── B11197_02_14.png │ ├── B11197_02_15.png │ ├── B11197_02_16.png │ ├── B11197_02_17.png │ ├── B11197_02_18.png │ ├── B11197_02_19.odg │ ├── B11197_02_19.png │ ├── B11197_02_20.png │ ├── B11197_02_21.png │ └── B11197_02_22.png ├── Chp3 │ ├── B11197_03_01.odg │ ├── B11197_03_01.png │ ├── B11197_03_02.png │ ├── B11197_03_03.png │ ├── B11197_03_04.png │ ├── B11197_03_05.png │ ├── B11197_03_06.png │ ├── B11197_03_07.png │ ├── B11197_03_08.png │ ├── B11197_03_10.png │ ├── B11197_03_11.png │ ├── B11197_03_12.png │ ├── B11197_03_13.png │ ├── B11197_03_14.png │ ├── B11197_03_15.png │ ├── B11197_03_16.png │ ├── B11197_03_17.png │ ├── B11197_03_18.png │ ├── B11197_03_19.png │ ├── B11197_03_20.png │ ├── B11197_03_21.png │ ├── B11197_03_22.png │ ├── B11197_03_23.png │ ├── B11197_03_24.png │ ├── B11197_03_25.png │ ├── B11197_03_26.png │ ├── B11197_03_27.png │ ├── B11197_03_28.png │ ├── B11197_03_29.png │ ├── B11197_03_30.png │ └── B11197_03_9.png ├── Chp4 │ ├── B11197_04_01.png │ ├── B11197_04_02.png │ ├── B11197_04_03.png │ ├── B11197_04_04.png │ ├── B11197_04_05.png │ ├── B11197_04_06.png │ ├── B11197_04_07.png │ ├── B11197_04_08.png │ ├── B11197_04_09.png │ ├── B11197_04_10.png │ ├── B11197_04_11.png │ ├── B11197_04_12.png │ └── B11197_04_13.png ├── Chp5 │ ├── B11197_05_01.png │ ├── B11197_05_02.png │ ├── B11197_05_03.png │ ├── B11197_05_04.png │ ├── B11197_05_05.png │ ├── B11197_05_06.png │ ├── B11197_05_07.png │ ├── B11197_05_08.png │ ├── B11197_05_09.png │ ├── B11197_05_11.png │ ├── B11197_05_12.png │ ├── B11197_05_13.png │ ├── B11197_05_14.png │ └── B11197_05_15.png ├── Chp6 │ ├── B11197_06_01.png │ ├── B11197_06_02.png │ ├── B11197_06_03.png │ ├── B11197_06_04.png │ ├── B11197_06_06.png │ ├── B11197_06_07.png │ ├── B11197_06_08.png │ ├── B11197_06_09.png │ ├── B11197_06_10.png │ ├── B11197_06_11.png │ ├── B11197_06_12.png │ ├── B11197_06_13.png │ ├── B11197_06_14.png │ └── B11197_06_15.png ├── Chp7 │ ├── B11197_07_01.png │ ├── B11197_07_02.png │ ├── B11197_07_03.png │ ├── B11197_07_04.png │ ├── B11197_07_05.png │ ├── B11197_07_06.png │ ├── B11197_07_07.png │ ├── B11197_07_08.png │ ├── B11197_07_09.png │ ├── B11197_07_10.png │ ├── B11197_07_11.png │ ├── B11197_07_12.png │ ├── B11197_07_13.png │ ├── B11197_07_14.png │ ├── B11197_07_15.png │ ├── B11197_07_16.png │ └── B11197_07_17.png ├── Chp8 │ ├── B11197_08_01.png │ ├── B11197_08_03.png │ ├── B11197_08_04.png │ ├── B11197_08_05.png │ ├── B11197_08_06.png │ ├── B11197_08_07.png │ ├── B11197_08_08.png │ ├── B11197_08_10.png │ ├── B11197_08_11.png │ ├── B11197_08_12.png │ ├── B11197_08_13.png │ ├── B11197_08_14.png │ ├── B11197_08_02_grid.png │ ├── B11197_08_06_smc.png │ └── B11197_08_09_trace.png └── data │ ├── chemical_shifts.csv │ ├── dummy.csv │ ├── space_flu.csv │ ├── islands_dist.csv │ ├── anscombe.csv │ ├── islands.csv │ ├── coal.csv │ ├── iris.csv │ ├── redwood.csv │ ├── tips.csv │ ├── babies.csv │ ├── fish.csv │ └── howell.csv ├── first_edition ├── Figures │ ├── B04958_01_06.odg │ ├── B04958_01_06.png │ ├── B04958_03_06.odg │ ├── B04958_03_06.png │ ├── B04958_03_11.odg │ ├── B04958_03_11.png │ ├── B04958_04_01.odg │ ├── B04958_04_01.png │ ├── B04958_05_11.odg │ ├── B04958_05_11.png │ ├── B04958_06_08.odg │ ├── B04958_06_08.png │ ├── B04958_07_04.odg │ ├── B04958_07_04.png │ ├── 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-------------------------------------------------------------------------------- /code/data/chemical_shifts.csv: -------------------------------------------------------------------------------- 1 | 51.06 2 | 55.12 3 | 53.73 4 | 50.24 5 | 52.05 6 | 56.40 7 | 48.45 8 | 52.34 9 | 55.65 10 | 51.49 11 | 51.86 12 | 63.43 13 | 53.00 14 | 56.09 15 | 51.93 16 | 52.31 17 | 52.33 18 | 57.48 19 | 57.44 20 | 55.14 21 | 53.93 22 | 54.62 23 | 56.09 24 | 68.58 25 | 51.36 26 | 55.47 27 | 50.73 28 | 51.94 29 | 54.95 30 | 50.39 31 | 52.91 32 | 51.50 33 | 52.68 34 | 47.72 35 | 49.73 36 | 51.82 37 | 54.99 38 | 52.84 39 | 53.19 40 | 54.52 41 | 51.46 42 | 53.73 43 | 51.61 44 | 49.81 45 | 52.42 46 | 54.30 47 | 53.84 48 | 53.16 49 | -------------------------------------------------------------------------------- /first_edition/README.md: -------------------------------------------------------------------------------- 1 | # Bayesian Analysis with Python 2 | 3 | In this repository you will find the errata for the book [Bayesian Analysis with Python](https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python). Also the code from the book have been updated to run with the last version of PyMC3 (3.3). 4 | 5 | 6 | The code has been tested with the following packages' versions: 7 | 8 | Python 3.6.2 9 | IPython 6.1.0 10 | PyMC3 3.2 11 | NumPy 1.13.3 12 | SciPy 1.0.0 13 | Matplotlib 2.1.0 14 | Seaborn 0.8.0 15 | Pandas 0.20.3 16 | -------------------------------------------------------------------------------- /code/data/dummy.csv: -------------------------------------------------------------------------------- 1 | -1.081 9.357 2 | -0.888 8.322 3 | -0.623 8.745 4 | -0.480 7.346 5 | -0.505 7.028 6 | -0.345 4.032 7 | 0.052 1.490 8 | 0.061 3.265 9 | 0.215 4.525 10 | 0.395 3.339 11 | 0.643 2.045 12 | 0.871 -1.336 13 | 0.776 1.264 14 | 0.833 -0.813 15 | 1.224 -0.335 16 | 1.300 0.152 17 | 1.563 -1.797 18 | 1.669 -4.772 19 | 1.967 -3.043 20 | 1.950 -5.293 21 | 2.030 -4.401 22 | 2.284 -5.294 23 | 2.534 -2.912 24 | 2.531 -3.680 25 | 2.765 -3.975 26 | 2.913 -7.202 27 | 3.168 -4.317 28 | 3.119 -7.854 29 | 3.385 -5.167 30 | 3.488 -5.135 31 | 3.538 -6.754 32 | 3.900 -6.662 33 | 3.970 -6.080 34 | -------------------------------------------------------------------------------- /code/data/space_flu.csv: -------------------------------------------------------------------------------- 1 | age,space_flu 2 | 49,0 3 | 22,0 4 | 10,1 5 | 25,1 6 | 79,1 7 | 33,0 8 | 08,0 9 | 78,1 10 | 57,1 11 | 79,1 12 | 32,0 13 | 47,0 14 | 06,1 15 | 51,0 16 | 11,1 17 | 65,1 18 | 72,1 19 | 29,0 20 | 32,0 21 | 49,0 22 | 68,1 23 | 20,0 24 | 42,0 25 | 75,1 26 | 76,1 27 | 07,1 28 | 28,0 29 | 24,0 30 | 61,1 31 | 70,1 32 | 60,0 33 | 45,0 34 | 22,0 35 | 49,0 36 | 25,0 37 | 68,1 38 | 00,1 39 | 26,0 40 | 22,0 41 | 78,1 42 | 37,0 43 | 24,0 44 | 20,0 45 | 30,0 46 | 13,1 47 | 07,1 48 | 25,0 49 | 33,0 50 | 50,0 51 | 31,0 52 | 10,1 53 | 42,0 54 | 33,0 55 | 30,0 56 | 31,0 57 | 33,0 58 | 22,0 59 | 39,0 60 | 30,0 61 | 27,1 62 | -------------------------------------------------------------------------------- /code/data/islands_dist.csv: -------------------------------------------------------------------------------- 1 | "","Ml","Ti","SC","Ya","Fi","Tr","Ch","Mn","To","Ha" 2 | "Malekula",0,0.475,0.631,4.363,1.234,2.036,3.178,2.794,1.86,5.678 3 | "Tikopia",0.475,0,0.315,4.173,1.236,2.007,2.877,2.67,1.965,5.283 4 | "Santa Cruz",0.631,0.315,0,3.859,1.55,1.708,2.588,2.356,2.279,5.401 5 | "Yap",4.363,4.173,3.859,0,5.391,2.462,1.555,1.616,6.136,7.178 6 | "Lau Fiji",1.234,1.236,1.55,5.391,0,3.219,4.027,3.906,0.763,4.884 7 | "Trobriand",2.036,2.007,1.708,2.462,3.219,0,1.801,0.85,3.893,6.653 8 | "Chuuk",3.178,2.877,2.588,1.555,4.027,1.801,0,1.213,4.789,5.787 9 | "Manus",2.794,2.67,2.356,1.616,3.906,0.85,1.213,0,4.622,6.722 10 | "Tonga",1.86,1.965,2.279,6.136,0.763,3.893,4.789,4.622,0,5.037 11 | "Hawaii",5.678,5.283,5.401,7.178,4.884,6.653,5.787,6.722,5.037,0 12 | -------------------------------------------------------------------------------- /code/data/anscombe.csv: -------------------------------------------------------------------------------- 1 | group,x,y 2 | I,10.0,8.04 3 | I,8.0,6.95 4 | I,13.0,7.58 5 | I,9.0,8.81 6 | I,11.0,8.33 7 | I,14.0,9.96 8 | I,6.0,7.24 9 | I,4.0,4.26 10 | I,12.0,10.84 11 | I,7.0,4.82 12 | I,5.0,5.68 13 | II,10.0,9.14 14 | II,8.0,8.14 15 | II,13.0,8.74 16 | II,9.0,8.77 17 | II,11.0,9.26 18 | II,14.0,8.1 19 | II,6.0,6.13 20 | II,4.0,3.1 21 | II,12.0,9.13 22 | II,7.0,7.26 23 | II,5.0,4.74 24 | III,10.0,7.46 25 | III,8.0,6.77 26 | III,13.0,12.74 27 | III,9.0,7.11 28 | III,11.0,7.81 29 | III,14.0,8.84 30 | III,6.0,6.08 31 | III,4.0,5.39 32 | III,12.0,8.15 33 | III,7.0,6.42 34 | III,5.0,5.73 35 | IV,8.0,6.58 36 | IV,8.0,5.76 37 | IV,8.0,7.71 38 | IV,8.0,8.84 39 | IV,8.0,8.47 40 | IV,8.0,7.04 41 | IV,8.0,5.25 42 | IV,19.0,12.5 43 | IV,8.0,5.56 44 | IV,8.0,7.91 45 | IV,8.0,6.89 46 | -------------------------------------------------------------------------------- /code/data/islands.csv: -------------------------------------------------------------------------------- 1 | "culture","population","contact","total_tools","mean_TU","lat","lon","lon2","logpop" 2 | "Malekula",1100,"low",13,3.2,-16.3,167.5,-12.5,7.00306545878646 3 | "Tikopia",1500,"low",22,4.7,-12.3,168.8,-11.2,7.3132203870903 4 | "Santa Cruz",3600,"low",24,4,-10.7,166,-14,8.1886891244442 5 | "Yap",4791,"high",43,5,9.5,138.1,-41.9,8.47449443688312 6 | "Lau Fiji",7400,"high",33,5,-17.7,178.1,-1.90000000000001,8.90923527919226 7 | "Trobriand",8000,"high",19,4,-8.7,150.9,-29.1,8.98719682066197 8 | "Chuuk",9200,"high",40,3.8,7.4,151.6,-28.4,9.12695876303713 9 | "Manus",13000,"low",28,6.6,-2.1,146.9,-33.1,9.47270463644367 10 | "Tonga",17500,"high",55,5.4,-21.2,-175.2,4.80000000000001,9.76995615991161 11 | "Hawaii",275000,"low",71,6.6,19.9,-155.6,24.4,12.5245263766487 12 | 13 | -------------------------------------------------------------------------------- /first_edition/Errata/Chp_08.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Page 234\n", 8 | "\n", 9 | "The sentence:\n", 10 | " \n", 11 | "`So, we will define parametric models as those models for which the number of parameters is allowed to grow with the size of the data.`\n", 12 | "\n", 13 | "Should be:\n", 14 | " \n", 15 | "`So, we will define non-parametric models as those models for which the number of parameters is allowed to grow with the size of the data.`" 16 | ] 17 | } 18 | ], 19 | "metadata": { 20 | "kernelspec": { 21 | "display_name": "Python 3", 22 | "language": "python", 23 | "name": "python3" 24 | }, 25 | "language_info": { 26 | "codemirror_mode": { 27 | "name": "ipython", 28 | "version": 3 29 | }, 30 | "file_extension": ".py", 31 | "mimetype": "text/x-python", 32 | "name": "python", 33 | "nbconvert_exporter": "python", 34 | "pygments_lexer": "ipython3", 35 | "version": "3.6.2" 36 | } 37 | }, 38 | "nbformat": 4, 39 | "nbformat_minor": 2 40 | } 41 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Osvaldo Martin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /extras/SopaSeca.md: -------------------------------------------------------------------------------- 1 | # Sopa Seca (Dry soup) 2 | 3 | My mother likes to prepare Sopa Seca on Sundays as a side-dish for asado (barbecue). When I grow-up I learn this is not an Argentinian tradition, just something my mother, and the rest of my family like. 4 | 5 | ![sopa seca](sopa_seca.jpeg) 6 | 7 | ## Ingredients: 8 | 9 | * 4 large garlic cloves 10 | * 1 medium onion 11 | * 2 bay leaves 12 | * 3 tablespoons of sweet paprika 13 | * 200 grams of spaghetti 14 | * 1 tablespoon of salt 15 | * black pepper to taste (typically, Argentinian food is not spicy) 16 | * 1 liter of boiling water 17 | * 4 tablespoons of oil (sunflower or olive) 18 | 19 | 20 | ## Instructions: 21 | 22 | Chop the garlic and onion into very small pieces. Add cooking oil to a saucepan and bring to low heat, add the chopped garlic, chopped onion, bay leaves, salt and pepper and bring to low heat. Let it cook gently until the onion becomes transparent. Add the spaghetti and the boiling water, just the necessary to barely cover the noodles. Add the paprika. Stir gently and continue cooking at low heat for 10 to 12 minutes, keep adding water, just enough to cover the noodles. Turn off the flame and let rest for a few minutes before serving. 23 | 24 | 25 | -------------------------------------------------------------------------------- /first_edition/Errata/Chp_06.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Page 189\n", 8 | "\n", 9 | "The formula:\n", 10 | "\n", 11 | "$WAIC = 2 lppd + 2p_{WAIC}$\n", 12 | "\n", 13 | "Should be:\n", 14 | " \n", 15 | "$WAIC = -2 lppd + 2p_{WAIC}$" 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": {}, 21 | "source": [ 22 | "## Page 201\n", 23 | "\n", 24 | "The sentence:\n", 25 | " \n", 26 | "`This makes total sense since the data has fewer values of heads than expected for θ = 5`\n", 27 | "\n", 28 | "Should be:\n", 29 | " \n", 30 | "`This makes total sense since the data has fewer values of heads than expected for θ = 0.5`" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": null, 36 | "metadata": { 37 | "collapsed": true 38 | }, 39 | "outputs": [], 40 | "source": [] 41 | } 42 | ], 43 | "metadata": { 44 | "kernelspec": { 45 | "display_name": "Python 3", 46 | "language": "python", 47 | "name": "python3" 48 | }, 49 | "language_info": { 50 | "codemirror_mode": { 51 | "name": "ipython", 52 | "version": 3 53 | }, 54 | "file_extension": ".py", 55 | "mimetype": "text/x-python", 56 | "name": "python", 57 | "nbconvert_exporter": "python", 58 | "pygments_lexer": "ipython3", 59 | "version": "3.6.2" 60 | } 61 | }, 62 | "nbformat": 4, 63 | "nbformat_minor": 2 64 | } 65 | -------------------------------------------------------------------------------- /first_edition/Errata/Chp_03.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Page 65 and page 66\n", 8 | "\n", 9 | "$\\sigma_h$ and $\\sigma_{\\sigma}$ are the same variable. \n", 10 | "\n", 11 | "\n", 12 | "Thus\n", 13 | "\n", 14 | "#### page 65\n", 15 | "\n", 16 | "the line:\n", 17 | "\n", 18 | "$$\\sigma \\sim HalfNormal(\\sigma_{h})$$\n", 19 | "\n", 20 | "\n", 21 | "should be:\n", 22 | " \n", 23 | "$$\\sigma \\sim HalfNormal(\\sigma_{\\sigma})$$\n", 24 | "\n", 25 | "#### page 66 \n", 26 | "\n", 27 | "the line:\n", 28 | "\n", 29 | "and $\\sigma$ comes from a half-normal distribution with standard deviation $\\sigma_h$\n", 30 | "\n", 31 | "should be:\n", 32 | "\n", 33 | "and $\\sigma$ comes from a half-normal distribution with standard deviation $\\sigma_{\\sigma}$\n", 34 | "\n", 35 | "#### also page 66\n", 36 | "\n", 37 | "The line:\n", 38 | "\n", 39 | "`For the half normal, we will use a value of $\\sigma_{\\sigma}$ is equal to 10, just a large value for the data.`\n", 40 | "\n", 41 | "should be:\n", 42 | "\n", 43 | "`For the half-normal, we will use a value of $\\sigma_{\\sigma}$ equal to 10, just a large value for the data.`" 44 | ] 45 | } 46 | ], 47 | "metadata": { 48 | "kernelspec": { 49 | "display_name": "Python 3", 50 | "language": "python", 51 | "name": "python3" 52 | }, 53 | "language_info": { 54 | "codemirror_mode": { 55 | "name": "ipython", 56 | "version": 3 57 | }, 58 | "file_extension": ".py", 59 | "mimetype": "text/x-python", 60 | "name": "python", 61 | "nbconvert_exporter": "python", 62 | "pygments_lexer": "ipython3", 63 | "version": "3.6.2" 64 | } 65 | }, 66 | "nbformat": 4, 67 | "nbformat_minor": 2 68 | } 69 | -------------------------------------------------------------------------------- /first_edition/Errata/Chp_05.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Page 165\n", 8 | "\n", 9 | "the line:\n", 10 | "\n", 11 | "`The ANalysis Of VAriance (ANOVA), where we have a quantitative predicted variable and more than two categorical predictors.`\n", 12 | "\n", 13 | "should be:\n", 14 | "\n", 15 | "`The ANalysis Of VAriance (ANOVA), where we have a quantitative predicted variable and more than or equal to one categorical predictor.`\n", 16 | "\n", 17 | "Note: One-way ANOVA needs a quantitative predected variable and one categorical predictor. If the categorical predictor has two values, One-way ANOVA almost equals to the method of testing the difference of means between two populations. If it has more than two values, this must be the usual one-way ANOVA." 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "### Page 171\n", 25 | "\n", 26 | "the line:\n", 27 | "\n", 28 | "`...In all cases, we tried to directly compute p(x | y), that is, the probability of a given class knowing x...`\n", 29 | "\n", 30 | "should be:\n", 31 | "\n", 32 | "`...In all cases, we tried to directly compute p(y | x), that is, the probability of a given class knowing x...`" 33 | ] 34 | } 35 | ], 36 | "metadata": { 37 | "kernelspec": { 38 | "display_name": "Python 3", 39 | "language": "python", 40 | "name": "python3" 41 | }, 42 | "language_info": { 43 | "codemirror_mode": { 44 | "name": "ipython", 45 | "version": 3 46 | }, 47 | "file_extension": ".py", 48 | "mimetype": "text/x-python", 49 | "name": "python", 50 | "nbconvert_exporter": "python", 51 | "pygments_lexer": "ipython3", 52 | "version": "3.6.2" 53 | } 54 | }, 55 | "nbformat": 4, 56 | "nbformat_minor": 2 57 | } 58 | -------------------------------------------------------------------------------- /first_edition/code/Chp1/hpd.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | import numpy as np 3 | import scipy.stats.kde as kde 4 | 5 | def hpd_grid(sample, alpha=0.05, roundto=2): 6 | """Calculate highest posterior density (HPD) of array for given alpha. 7 | The HPD is the minimum width Bayesian credible interval (BCI). 8 | The function works for multimodal distributions, returning more than one mode 9 | 10 | Parameters 11 | ---------- 12 | 13 | sample : Numpy array or python list 14 | An array containing MCMC samples 15 | alpha : float 16 | Desired probability of type I error (defaults to 0.05) 17 | roundto: integer 18 | Number of digits after the decimal point for the results 19 | 20 | Returns 21 | ---------- 22 | hpd: array with the lower 23 | 24 | """ 25 | sample = np.asarray(sample) 26 | sample = sample[~np.isnan(sample)] 27 | # get upper and lower bounds 28 | l = np.min(sample) 29 | u = np.max(sample) 30 | density = kde.gaussian_kde(sample) 31 | x = np.linspace(l, u, 2000) 32 | y = density.evaluate(x) 33 | #y = density.evaluate(x, l, u) waitting for PR to be accepted 34 | xy_zipped = zip(x, y/np.sum(y)) 35 | xy = sorted(xy_zipped, key=lambda x: x[1], reverse=True) 36 | xy_cum_sum = 0 37 | hdv = [] 38 | for val in xy: 39 | xy_cum_sum += val[1] 40 | hdv.append(val[0]) 41 | if xy_cum_sum >= (1-alpha): 42 | break 43 | hdv.sort() 44 | diff = (u-l)/20 # differences of 5% 45 | hpd = [] 46 | hpd.append(round(min(hdv), roundto)) 47 | for i in range(1, len(hdv)): 48 | if hdv[i]-hdv[i-1] >= diff: 49 | hpd.append(round(hdv[i-1], roundto)) 50 | hpd.append(round(hdv[i], roundto)) 51 | hpd.append(round(max(hdv), roundto)) 52 | ite = iter(hpd) 53 | hpd = list(zip(ite, ite)) 54 | modes = [] 55 | for value in hpd: 56 | x_hpd = x[(x > value[0]) & (x < value[1])] 57 | y_hpd = y[(x > value[0]) & (x < value[1])] 58 | modes.append(round(x_hpd[np.argmax(y_hpd)], roundto)) 59 | return hpd, x, y, modes 60 | -------------------------------------------------------------------------------- /first_edition/Errata/Chp_02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Page 34\n", 8 | "\n", 9 | "the line:\n", 10 | "\n", 11 | "`grid, posterior = posterior_grid_approx(points, h, n)`\n", 12 | "\n", 13 | "should be:\n", 14 | "\n", 15 | "`grid, posterior = posterior_grid(points, h, n)`\n", 16 | "\n", 17 | "The code is correct in the notebook." 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "### Page 39\n", 25 | "\n", 26 | "the line:\n", 27 | "\n", 28 | "`The most popular method that guarantees detailed balance is the Metropolis-Hasting algorithm.`\n", 29 | "\n", 30 | "should be:\n", 31 | "\n", 32 | "`The most popular method that guarantees detailed balance is the Metropolis-Hastings algorithm.`" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": { 38 | "collapsed": true 39 | }, 40 | "source": [ 41 | "### Page 50\n", 42 | "\n", 43 | "The line:\n", 44 | "\n", 45 | "`Often, as the number of data increases, the distribution of each parameter will tend to become Gaussian-like; this is due to the law of the large numbers.`\n", 46 | "\n", 47 | "should say:\n", 48 | "\n", 49 | "`Often, as the number of data increases, the distribution of each parameter will tend to become Gaussian-like; this is due to the central limit theorem.`\n", 50 | "\n", 51 | "Note: The central limit theorem states that (under certain conditions) when independent random variables are averaged we tend to get a Gaussian distribution, notice than the marginal distribution for a parameter is obtained by averaging over all the other parameters." 52 | ] 53 | } 54 | ], 55 | "metadata": { 56 | "kernelspec": { 57 | "display_name": "Python 3", 58 | "language": "python", 59 | "name": "python3" 60 | }, 61 | "language_info": { 62 | "codemirror_mode": { 63 | "name": "ipython", 64 | "version": 3 65 | }, 66 | "file_extension": ".py", 67 | "mimetype": "text/x-python", 68 | "name": "python", 69 | "nbconvert_exporter": "python", 70 | "pygments_lexer": "ipython3", 71 | "version": "3.6.2" 72 | } 73 | }, 74 | "nbformat": 4, 75 | "nbformat_minor": 2 76 | } 77 | -------------------------------------------------------------------------------- /first_edition/Errata/Chp_01.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Page 5\n", 8 | "\n", 9 | "The sentence:\n", 10 | "\n", 11 | "`...the probability of the mass of the electron being 9.1 x 10-31 kg...`\n", 12 | "\n", 13 | "should be:\n", 14 | "\n", 15 | "`...the probability of the mass of the electron being $9.1 \\times 10^{-31}$ kg...`" 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": {}, 21 | "source": [ 22 | "### Page 11\n", 23 | "\n", 24 | "It says: \n", 25 | "\n", 26 | "`p(D|H) is not necessarily the same as **p(D|H)**`\n", 27 | "\n", 28 | "It should be: \n", 29 | "\n", 30 | "`p(H|D) is not necessarily the same as **p(D|H)**`" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": { 36 | "collapsed": true 37 | }, 38 | "source": [ 39 | "### Page 14 \n", 40 | "\n", 41 | "**Choosing the likelihood**\n", 42 | "\n", 43 | "The correct formula shoul be:\n", 44 | "\n", 45 | "$$p(y|\\theta) = \\frac{N!}{y!(N-y)!} \\theta^y (1 - \\theta)^{N-y}$$" 46 | ] 47 | }, 48 | { 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "### Page 16\n", 53 | "\n", 54 | "**Choosing the prior**\n", 55 | "\n", 56 | "Parentheses are missing in book's formula:\n", 57 | "\n", 58 | "$$\n", 59 | "p(\\theta)= \\frac{\\Gamma(\\alpha+\\beta)}{\\Gamma(\\alpha)\\Gamma(\\beta)}\\, \\theta^{\\alpha-1}(1-\\theta)^{\\beta-1}\n", 60 | "$$" 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": {}, 66 | "source": [ 67 | "### Page 18\n", 68 | "\n", 69 | "**Getting the posterior**\n", 70 | "\n", 71 | "Parentheses are missing in book's formula:\n", 72 | "\n", 73 | "$$p(y|\\theta) = \\frac{N!}{y!(N-y)!} \\theta^y (1 - \\theta)^{N−y} \\frac{\\Gamma(\\alpha+\\beta)}{\\Gamma(\\alpha)\\Gamma(\\beta)}\\, \\theta^{\\alpha-1}(1-\\theta)^{\\beta-1}$$\n", 74 | "\n", 75 | "\n", 76 | "$$\n", 77 | "p(\\theta|y) \\propto \\theta^y (1 - \\theta)^{N−y} \\theta^{\\alpha-1}(1-\\theta)^{\\beta-1}\n", 78 | "$$\n", 79 | "\n", 80 | "\n", 81 | "$$\n", 82 | "p(\\theta|y) \\propto \\theta^{\\alpha-1+y}(1-\\theta)^{\\beta+N−y-1}\n", 83 | "$$" 84 | ] 85 | } 86 | ], 87 | "metadata": { 88 | "kernelspec": { 89 | "display_name": "Python 3", 90 | "language": "python", 91 | "name": "python3" 92 | }, 93 | "language_info": { 94 | "codemirror_mode": { 95 | "name": "ipython", 96 | "version": 3 97 | }, 98 | "file_extension": ".py", 99 | "mimetype": "text/x-python", 100 | "name": "python", 101 | "nbconvert_exporter": "python", 102 | "pygments_lexer": "ipython3", 103 | "version": "3.6.2" 104 | } 105 | }, 106 | "nbformat": 4, 107 | "nbformat_minor": 2 108 | } 109 | -------------------------------------------------------------------------------- /code/data/coal.csv: -------------------------------------------------------------------------------- 1 | 1.8512026e+03 2 | 1.8516324e+03 3 | 1.8519692e+03 4 | 1.8519747e+03 5 | 1.8523142e+03 6 | 1.8523470e+03 7 | 1.8523580e+03 8 | 1.8523854e+03 9 | 1.8529767e+03 10 | 1.8531958e+03 11 | 1.8532286e+03 12 | 1.8533190e+03 13 | 1.8534997e+03 14 | 1.8541348e+03 15 | 1.8563963e+03 16 | 1.8565058e+03 17 | 1.8565387e+03 18 | 1.8566181e+03 19 | 1.8571383e+03 20 | 1.8574038e+03 21 | 1.8575818e+03 22 | 1.8580910e+03 23 | 1.8581540e+03 24 | 1.8584059e+03 25 | 1.8589452e+03 26 | 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1.9415736e+03 180 | 1.9420007e+03 181 | 1.9421294e+03 182 | 1.9424825e+03 183 | 1.9469452e+03 184 | 1.9470246e+03 185 | 1.9476188e+03 186 | 1.9476379e+03 187 | 1.9476872e+03 188 | 1.9514052e+03 189 | 1.9578830e+03 190 | 1.9604894e+03 191 | 1.9622197e+03 192 | -------------------------------------------------------------------------------- /first_edition/code/Chp1/plot_post.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | import numpy as np 3 | from scipy import stats 4 | import matplotlib.pyplot as plt 5 | from hpd import hpd_grid 6 | 7 | 8 | def plot_post(sample, alpha=0.05, show_mode=True, kde_plot=True, bins=50, 9 | ROPE=None, comp_val=None, roundto=2): 10 | """Plot posterior and HPD 11 | 12 | Parameters 13 | ---------- 14 | 15 | sample : Numpy array or python list 16 | An array containing MCMC samples 17 | alpha : float 18 | Desired probability of type I error (defaults to 0.05) 19 | show_mode: Bool 20 | If True the legend will show the mode(s) value(s), if false the mean(s) 21 | will be displayed 22 | kde_plot: Bool 23 | If True the posterior will be displayed using a Kernel Density Estimation 24 | otherwise an histogram will be used 25 | bins: integer 26 | Number of bins used for the histogram, only works when kde_plot is False 27 | ROPE: list or numpy array 28 | Lower and upper values of the Region Of Practical Equivalence 29 | comp_val: float 30 | Comparison value 31 | 32 | 33 | Returns 34 | ------- 35 | 36 | post_summary : dictionary 37 | Containing values with several summary statistics 38 | 39 | """ 40 | 41 | post_summary = {'mean':0,'median':0,'mode':0, 'alpha':0,'hpd_low':0, 42 | 'hpd_high':0, 'comp_val':0, 'pc_gt_comp_val':0, 'ROPE_low':0, 43 | 'ROPE_high':0, 'pc_in_ROPE':0} 44 | 45 | post_summary['mean'] = round(np.mean(sample), roundto) 46 | post_summary['median'] = round(np.median(sample), roundto) 47 | post_summary['alpha'] = alpha 48 | 49 | # Compute the hpd, KDE and mode for the posterior 50 | hpd, x, y, modes = hpd_grid(sample, alpha, roundto) 51 | post_summary['hpd'] = hpd 52 | post_summary['mode'] = modes 53 | 54 | ## Plot KDE. 55 | if kde_plot: 56 | plt.plot(x, y, color='k', lw=2) 57 | ## Plot histogram. 58 | else: 59 | plt.hist(sample, normed=True, bins=bins, facecolor='b', 60 | edgecolor='w') 61 | 62 | ## Display mode or mean: 63 | if show_mode: 64 | string = '{:g} ' * len(post_summary['mode']) 65 | plt.plot(0, label='mode =' + string.format(*post_summary['mode']), alpha=0) 66 | else: 67 | plt.plot(0, label='mean = {:g}'.format(post_summary['mean']), alpha=0) 68 | 69 | ## Display the hpd. 70 | hpd_label = '' 71 | for value in hpd: 72 | plt.plot(value, [0, 0], linewidth=10, color='b') 73 | hpd_label = hpd_label + '{:g} {:g}\n'.format(round(value[0], roundto), round(value[1], roundto)) 74 | plt.plot(0, 0, linewidth=4, color='b', label='hpd {:g}%\n{}'.format((1-alpha)*100, hpd_label)) 75 | ## Display the ROPE. 76 | if ROPE is not None: 77 | pc_in_ROPE = round(np.sum((sample > ROPE[0]) & (sample < ROPE[1]))/len(sample)*100, roundto) 78 | plt.plot(ROPE, [0, 0], linewidth=20, color='r', alpha=0.75) 79 | plt.plot(0, 0, linewidth=4, color='r', label='{:g}% in ROPE'.format(pc_in_ROPE)) 80 | post_summary['ROPE_low'] = ROPE[0] 81 | post_summary['ROPE_high'] = ROPE[1] 82 | post_summary['pc_in_ROPE'] = pc_in_ROPE 83 | ## Display the comparison value. 84 | if comp_val is not None: 85 | pc_gt_comp_val = round(100 * np.sum(sample > comp_val)/len(sample), roundto) 86 | pc_lt_comp_val = round(100 - pc_gt_comp_val, roundto) 87 | plt.axvline(comp_val, ymax=.75, color='g', linewidth=4, alpha=0.75, 88 | label='{:g}% < {:g} < {:g}%'.format(pc_lt_comp_val, 89 | comp_val, pc_gt_comp_val)) 90 | post_summary['comp_val'] = comp_val 91 | post_summary['pc_gt_comp_val'] = pc_gt_comp_val 92 | 93 | plt.legend(loc=0, framealpha=1) 94 | frame = plt.gca() 95 | frame.axes.get_yaxis().set_ticks([]) 96 | return post_summary 97 | 98 | 99 | -------------------------------------------------------------------------------- /code/data/iris.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,species 2 | 5.1,3.5,1.4,0.2,setosa 3 | 4.9,3.0,1.4,0.2,setosa 4 | 4.7,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5.0,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5.0,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3.0,1.4,0.1,setosa 15 | 4.3,3.0,1.1,0.1,setosa 16 | 5.8,4.0,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1.0,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5.0,3.0,1.6,0.2,setosa 28 | 5.0,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.2,setosa 37 | 5.0,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.6,1.4,0.1,setosa 40 | 4.4,3.0,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5.0,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5.0,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3.0,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5.0,3.3,1.4,0.2,setosa 52 | 7.0,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4.0,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1.0,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5.0,2.0,3.5,1.0,versicolor 63 | 5.9,3.0,4.2,1.5,versicolor 64 | 6.0,2.2,4.0,1.0,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3.0,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1.0,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4.0,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3.0,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3.0,5.0,1.7,versicolor 80 | 6.0,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1.0,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1.0,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6.0,2.7,5.1,1.6,versicolor 86 | 5.4,3.0,4.5,1.5,versicolor 87 | 6.0,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3.0,4.1,1.3,versicolor 91 | 5.5,2.5,4.0,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3.0,4.6,1.4,versicolor 94 | 5.8,2.6,4.0,1.2,versicolor 95 | 5.0,2.3,3.3,1.0,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3.0,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3.0,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6.0,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3.0,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3.0,5.8,2.2,virginica 107 | 7.6,3.0,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2.0,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3.0,5.5,2.1,virginica 115 | 5.7,2.5,5.0,2.0,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3.0,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6.0,2.2,5.0,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2.0,virginica 124 | 7.7,2.8,6.7,2.0,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6.0,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3.0,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3.0,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2.0,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3.0,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6.0,3.0,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3.0,5.2,2.3,virginica 148 | 6.3,2.5,5.0,1.9,virginica 149 | 6.5,3.0,5.2,2.0,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3.0,5.1,1.8,virginica 152 | -------------------------------------------------------------------------------- /first_edition/Errata/Chp_04.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Page 102\n", 8 | "\n", 9 | "The formula\n", 10 | "\n", 11 | "$\\alpha = \\alpha' + \\beta' x$\n", 12 | "\n", 13 | "Should be:\n", 14 | "\n", 15 | "$\\alpha = \\alpha' - \\beta' x$\n", 16 | "\n", 17 | "\n", 18 | "I want to add a clarification to the book. \n", 19 | "\n", 20 | "We center $x$ by doing:\n", 21 | "\n", 22 | "$$x' = x - \\bar x$$\n", 23 | "\n", 24 | "Then we can write the linear model as:\n", 25 | "\n", 26 | "$$y = \\alpha' + \\beta' x' + \\epsilon$$\n", 27 | "\n", 28 | "if we replace $x'$ with $x - \\bar x$, we get:\n", 29 | "\n", 30 | "$$y = \\alpha' + \\beta' (x - \\bar x) + \\epsilon$$\n", 31 | "\n", 32 | "reordering we get:\n", 33 | "\n", 34 | "$$y = \\alpha' - \\beta' \\bar x + \\beta' x + \\epsilon$$\n", 35 | "\n", 36 | "Notice that this last equation is equivalent to \n", 37 | "\n", 38 | "$$y = \\alpha + \\beta x + \\epsilon$$\n", 39 | "\n", 40 | "where:\n", 41 | "\n", 42 | "$$\\alpha = \\alpha' - \\beta' \\bar x$$\n", 43 | "\n", 44 | "and\n", 45 | "\n", 46 | "$$\\beta = \\beta' $$" 47 | ] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": { 52 | "collapsed": true 53 | }, 54 | "source": [ 55 | "### Page 108\n", 56 | "\n", 57 | "The sentence:\n", 58 | "\n", 59 | "Notice that the formula resembles the once for the variance\n", 60 | "\n", 61 | "Should be:\n", 62 | "\n", 63 | "Notice that the formula resembles the one for the variance" 64 | ] 65 | }, 66 | { 67 | "cell_type": "markdown", 68 | "metadata": {}, 69 | "source": [ 70 | "### Page 129\n", 71 | "\n", 72 | "The sentence:\n", 73 | "\n", 74 | "When $x_i \\lt \\sim 11$, the dominating contribution comes from $\\beta_1$ , and when $x_i \\gt \\sim 11$ $\\beta_2$ dominates.\n", 75 | "\n", 76 | "Should be:\n", 77 | "\n", 78 | "When $x_i \\lt \\sim 2$, the dominating contribution comes from $\\beta_1$ , and when $x_i \\lt \\sim 2$ $\\beta_2$ dominates.\n" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | "### Page 131\n", 86 | "\n", 87 | "The sentence:\n", 88 | "\n", 89 | "Where $\\beta$ is a vector of coefficients of length $m$, that is, the number of dependent variables.\n", 90 | "\n", 91 | "Should be:\n", 92 | "\n", 93 | "Where $\\beta$ is a vector of coefficients of length $m$, that is, the number of independent variables." 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": {}, 99 | "source": [ 100 | "### Page 140\n", 101 | "\n", 102 | "The formula\n", 103 | "\n", 104 | "$\\mu = \\alpha + \\beta_1 x_1 + \\beta_1 x_2$\n", 105 | "\n", 106 | "Should be\n", 107 | "\n", 108 | "$\\mu = \\alpha + \\beta_1 x_1 + \\beta_2 x_2$\n" 109 | ] 110 | }, 111 | { 112 | "cell_type": "markdown", 113 | "metadata": {}, 114 | "source": [ 115 | "### Page 144\n", 116 | "\n", 117 | "The sentence:\n", 118 | "\n", 119 | "`Each dependent variable has an opposite effect on the dependent variable and the\n", 120 | "dependent variables are correlated`\n", 121 | "\n", 122 | "Should be:\n", 123 | " \n", 124 | "`Each independent variable has an opposite effect on the dependent variable and the\n", 125 | "independent variables are correlated`" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "### Page 145\n", 133 | "\n", 134 | "The sentence:\n", 135 | " \n", 136 | "In all the examples we have seen so far, the dependent variables contribute additively to the predicted variable;\n", 137 | "\n", 138 | "Should be:\n", 139 | "\n", 140 | "In all the examples we have seen so far, the independent variables contribute additively to the predicted variable;" 141 | ] 142 | } 143 | ], 144 | "metadata": { 145 | "kernelspec": { 146 | "display_name": "Python 3", 147 | "language": "python", 148 | "name": "python3" 149 | }, 150 | "language_info": { 151 | "codemirror_mode": { 152 | "name": "ipython", 153 | "version": 3 154 | }, 155 | "file_extension": ".py", 156 | "mimetype": "text/x-python", 157 | "name": "python", 158 | "nbconvert_exporter": "python", 159 | "pygments_lexer": "ipython3", 160 | "version": "3.6.2" 161 | } 162 | }, 163 | "nbformat": 4, 164 | "nbformat_minor": 2 165 | } 166 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Bayesian Analysis with Python (Second edition) 2 | 3 | This is the code repository for [Bayesian Analysis with Python](https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition), published by Packt. It contains all the code necessary to work through the book from start to finish. You can find the code from the first edition in the folder `first_edition` 4 | 5 | ## Feedback 6 | 7 | If you have read Bayesian Analysis with Python (second edition). I will really appreciate if you can answer this very brief [questionnaire](https://forms.gle/8wbUttTUHg3kwLHc8) 8 | 9 | 10 | ## Installation 11 | 12 | The code in the book was written using Python version 3.6. To install Python and Python libraries, I recommend using Anaconda, a scientific computing distribution. You can read more about Anaconda and download it at https:/​ / ​ www.​ anaconda.​ com/​ download/​ . This will install many useful Python packages on you system. You will need to install two more packages. To install PyMC3 please use conda : 13 | 14 | 15 | ``` 16 | conda install -c conda-forge pymc3 17 | ``` 18 | 19 | And for ArviZ you can do it with the following command: 20 | 21 | ``` 22 | pip install arviz 23 | ``` 24 | 25 | An alternative way to install the necessary packages, once Anaconda is installed in your system, is to go to https://github.com/aloctavodia/BAP and download the environment file named `bap.yml`. Using it, you can install all the necessary packages by doing: 26 | 27 | ``` 28 | conda env create -f bap.yml 29 | ``` 30 | 31 | For your reference the book was writting using the following python packages: 32 | 33 | * Ipython 7.0 34 | * Jupyter 1.0 (or Jupyter-lab 0.35) 35 | * NumPy 1.14.2 36 | * SciPy 1.1 37 | * Pandas 0.23.4 38 | * Matplotlib 3.0.2 39 | * Seaborn 0.9.0 40 | * ArviZ 0.3.1 41 | * PyMC3 3.6 42 | 43 | 44 | ## Errata 45 | 46 | If you find an error in the book please fill an issue or send a pull request. Please before reporting an error read the `errata.md` file and verify the error has not being reported before. 47 | 48 | 49 | ## Changes from first edition 50 | 51 | I have tried to incorporate all comments and critics from the first edition as well as much as possible input from students that have taken Bayesian courses with me. I have tried to remove those sections that I consider not very useful from the first edition and focus on improving and sometimes expanding those that contribute to understanding the basic elements of the Bayesian Analysis. I have added some new exercises and I try to provide useful summaries at the end of the book, I realized the ones from the first edition were almost useless. 52 | 53 | The code in the book has being updated to better reflect the new features of PyMC3. While the first edition was based on PyMC 3.0, the second edition uses PyMC 3.6. The books also relies on the new Python Library [ArviZ](https://arviz-devs.github.io/arviz/) (version 0.3.1) for summaries, diagnostics and most of the plots. 54 | 55 | 56 | This is a list of the most important differences per chapter. 57 | 58 | * Chapter 1: Thinking Probabilistically. The Chapter have been updated to ease the introduction of the basic concepts of probability and Bayesian statistics and its implications for data analysis. 59 | 60 | * Chapter 2: Programming Probabilistically. Chapter 2 and 3 from first edition have been unified and revised. This is now a more solid chapter setting the fundamentals of Bayesian statistics and probabilistic programming. New examples have been added. 61 | 62 | * Chapter 3: Modeling with Linear Regression. A new procedure to compute the R² score is explained. I have added a new example about performing linear regression with variable variance and using shared variables. 63 | 64 | * Chapter 4. Generalizing Linear Models. Content that was previously part of the mixture model chapter has been moved here providing a more coherent description of GLM models. 65 | 66 | * Chapter 5. Model comparison. The content has been revised. I have added a discussion of Bayesian p-values. A brief discussion of the entropy, KL divergence and it relation to WAIC and model averaging. A new example showing how to compute Bayes Factors with Sequential Monte Carlo is shown 67 | 68 | * Chapter 6 Mixture models. This chapter have been rewritten from scratch. The focus in on the basic elements of finite and infinite (Gaussian) mixture models. 69 | 70 | * Chapter 7 Gaussian process. This chapter have been rewritten from scratch. I am particularly happy with this chapter. New examples are used to intuitively explain what GP are before applying them. The chapter uses the GP module that is part of PyMC3. The new examples include regression, classification and Cox processes. 71 | 72 | * Chapter 8 Inference engine. The discussion of numerical methods to approximate the posterior and how to diagnose samples has its own chapter now. An explanation of how Sequential Monte Carlo works is also included. Although the chapter (and book) is focused on Markov Chain Monte Carlo methods, there is also a brief discussion about Variational Methods. 73 | 74 | * Chapter 9, Where To Go Next?, provides a list of resources for you to keep learning from beyond this book, and a very short _farewell speech_. 75 | 76 | 77 | The main aim from the first edition remains. That is, to help people with some Python experience but with no previous statistical knowledge to get started with Bayesian data analysis and probabilistic programming. 78 | -------------------------------------------------------------------------------- /code/data/redwood.csv: -------------------------------------------------------------------------------- 1 | 9.3148148e-01, 8.1767956e-01 2 | 9.3888889e-01, 7.6427256e-01 3 | 9.3518519e-01, 7.2191529e-01 4 | 9.7962963e-01, 6.6482505e-01 5 | 7.8703704e-01, 6.6114180e-01 6 | 8.4259259e-01, 6.4456722e-01 7 | 9.3888889e-01, 6.2246777e-01 8 | 7.3518519e-01, 6.1141805e-01 9 | 7.3148148e-01, 5.9668508e-01 10 | 8.3148148e-01, 5.5616943e-01 11 | 8.3333333e-01, 5.4327808e-01 12 | 9.3148148e-01, 5.7458564e-01 13 | 9.3888889e-01, 5.2486188e-01 14 | 5.9814815e-01, 4.9907919e-01 15 | 7.8333333e-01, 4.8802947e-01 16 | 9.0740741e-01, 4.5856354e-01 17 | 7.2592593e-01, 4.4935543e-01 18 | 5.9444444e-01, 4.4751381e-01 19 | 6.6111111e-01, 4.4567219e-01 20 | 8.7407407e-01, 4.4198895e-01 21 | 8.8888889e-01, 4.4014733e-01 22 | 6.6111111e-01, 4.3278085e-01 23 | 8.1481481e-01, 3.9779006e-01 24 | 5.8703704e-01, 3.9226519e-01 25 | 9.7962963e-01, 3.9226519e-01 26 | 9.6666667e-01, 3.8674033e-01 27 | 7.2592593e-01, 3.7384899e-01 28 | 7.8888889e-01, 3.7384899e-01 29 | 6.5370370e-01, 3.4254144e-01 30 | 5.9814815e-01, 3.3701657e-01 31 | 9.4259259e-01, 3.3149171e-01 32 | 7.2592593e-01, 3.0018416e-01 33 | 7.8518519e-01, 3.0018416e-01 34 | 5.4629630e-01, 2.8729282e-01 35 | 7.0925926e-01, 2.8360958e-01 36 | 9.0370370e-01, 2.7624309e-01 37 | 7.7962963e-01, 2.6335175e-01 38 | 7.7777778e-01, 2.4493554e-01 39 | 4.6296296e-01, 2.4493554e-01 40 | 4.7037037e-01, 2.3388582e-01 41 | 7.4814815e-01, 2.2467772e-01 42 | 8.8888889e-01, 2.1915285e-01 43 | 5.7962963e-01, 2.1731123e-01 44 | 9.5185185e-01, 2.0441989e-01 45 | 6.5740741e-01, 2.0073665e-01 46 | 6.7407407e-01, 1.8784530e-01 47 | 7.4444444e-01, 1.8784530e-01 48 | 7.2962963e-01, 1.8232044e-01 49 | 7.2037037e-01, 1.7311234e-01 50 | 6.7962963e-01, 1.6942910e-01 51 | 5.4444444e-01, 1.6574586e-01 52 | 9.1111111e-01, 1.5101289e-01 53 | 8.4444444e-01, 1.3627993e-01 54 | 4.2962963e-01, 1.1602210e-01 55 | 4.3518519e-01, 1.0497238e-01 56 | 5.5925926e-01, 1.0128913e-01 57 | 9.0925926e-01, 9.9447514e-02 58 | 9.6666667e-01, 9.5764273e-02 59 | 4.8518519e-01, 9.0239411e-02 60 | 8.2777778e-01, 8.1031308e-02 61 | 7.1111111e-01, 7.9189687e-02 62 | 8.8888889e-01, 7.9189687e-02 63 | 6.2407407e-01, 7.3664825e-02 64 | 8.8703704e-01, 6.6298343e-02 65 | 7.6111111e-01, 6.4456722e-02 66 | 8.9814815e-01, 5.8931860e-02 67 | 4.1111111e-01, 5.3406998e-02 68 | 9.7222222e-01, 5.1565378e-02 69 | 3.5740741e-01, 4.9723757e-02 70 | 9.5370370e-01, 4.0515654e-02 71 | 4.5925926e-01, 2.9465930e-02 72 | 8.2777778e-01, 2.7624309e-02 73 | 8.3148148e-01, 9.8895028e-01 74 | 8.4814815e-01, 9.8710866e-01 75 | 7.1111111e-01, 9.8342541e-01 76 | 8.6666667e-01, 9.7974217e-01 77 | 7.1111111e-01, 9.7053407e-01 78 | 7.1851852e-01, 9.5580110e-01 79 | 3.8888889e-02, 9.4659300e-01 80 | 7.1666667e-01, 9.4290976e-01 81 | 8.0000000e-01, 9.3001842e-01 82 | 6.1296296e-01, 9.2265193e-01 83 | 7.1111111e-01, 9.2081031e-01 84 | 7.6481481e-01, 9.1896869e-01 85 | 7.4074074e-01, 9.1344383e-01 86 | 7.9259259e-01, 9.1344383e-01 87 | 1.6296296e-01, 9.1344383e-01 88 | 7.0740741e-01, 9.0791897e-01 89 | 7.5555556e-01, 9.0423573e-01 90 | 2.3703704e-01, 9.0239411e-01 91 | 7.6851852e-01, 8.9871087e-01 92 | 6.7407407e-01, 8.9134438e-01 93 | 6.5555556e-01, 8.8766114e-01 94 | 7.1111111e-01, 8.8766114e-01 95 | 1.4074074e-01, 8.8766114e-01 96 | 6.9444444e-01, 8.8581952e-01 97 | 6.8148148e-01, 8.7845304e-01 98 | 1.3888889e-01, 8.7476980e-01 99 | 6.5740741e-01, 8.7292818e-01 100 | 6.7777778e-01, 8.6740331e-01 101 | 1.5555556e-01, 8.5082873e-01 102 | 3.9074074e-01, 8.4714549e-01 103 | 4.7407407e-01, 8.2688766e-01 104 | 3.9814815e-01, 7.9189687e-01 105 | 2.1851852e-01, 7.7900552e-01 106 | 1.2222222e-01, 7.7532228e-01 107 | 2.0555556e-01, 7.7163904e-01 108 | 2.6481481e-01, 7.6979742e-01 109 | 2.9629630e-01, 7.6058932e-01 110 | 3.0370370e-01, 7.5322284e-01 111 | 3.1296296e-01, 7.4401473e-01 112 | 2.5000000e-01, 7.4401473e-01 113 | 6.3703704e-01, 7.3480663e-01 114 | 2.7222222e-01, 7.3480663e-01 115 | 7.3148148e-01, 7.3112339e-01 116 | 6.2592593e-01, 7.2191529e-01 117 | 3.1111111e-01, 7.1639042e-01 118 | 6.2962963e-02, 7.1270718e-01 119 | 5.9444444e-01, 7.1270718e-01 120 | 6.1851852e-01, 7.0902394e-01 121 | 6.0185185e-01, 7.0718232e-01 122 | 2.2222222e-01, 7.0165746e-01 123 | 6.2777778e-01, 6.9797422e-01 124 | 2.3518519e-01, 6.9613260e-01 125 | 5.9814815e-01, 6.9613260e-01 126 | 2.8148148e-01, 6.9244936e-01 127 | 2.1851852e-01, 6.9244936e-01 128 | 2.4814815e-01, 6.9060773e-01 129 | 2.3148148e-01, 6.8508287e-01 130 | 4.7592593e-01, 6.7955801e-01 131 | 5.3888889e-01, 6.5193370e-01 132 | 1.9814815e-01, 6.2615101e-01 133 | 2.7777778e-01, 6.1694291e-01 134 | 5.3148148e-01, 6.0405157e-01 135 | 2.0555556e-01, 6.0036832e-01 136 | 5.2037037e-01, 5.9668508e-01 137 | 1.8518519e-01, 5.8931860e-01 138 | 1.7777778e-01, 5.6906077e-01 139 | 4.0370370e-01, 5.5616943e-01 140 | 4.7592593e-01, 5.1381215e-01 141 | 4.0740741e-01, 4.5672192e-01 142 | 2.3333333e-01, 4.4935543e-01 143 | 1.8703704e-01, 4.4751381e-01 144 | 4.1111111e-01, 4.4751381e-01 145 | 4.3148148e-01, 4.4751381e-01 146 | 2.2037037e-01, 4.4198895e-01 147 | 4.3703704e-01, 4.3646409e-01 148 | 2.4259259e-01, 4.3278085e-01 149 | 3.8333333e-01, 4.2725599e-01 150 | 2.3703704e-01, 4.2173112e-01 151 | 3.7037037e-01, 4.1988950e-01 152 | 4.2962963e-01, 4.1436464e-01 153 | 3.8333333e-01, 4.1068140e-01 154 | 4.0740741e-01, 3.9963168e-01 155 | 4.2777778e-01, 3.9594843e-01 156 | 3.3148148e-01, 3.7384899e-01 157 | 3.4259259e-01, 3.6648250e-01 158 | 3.3518519e-01, 3.5727440e-01 159 | 3.1666667e-01, 3.4806630e-01 160 | 3.2592593e-01, 3.3149171e-01 161 | 2.9259259e-01, 3.2965009e-01 162 | 3.0370370e-01, 3.2412523e-01 163 | 3.1666667e-01, 3.2044199e-01 164 | 8.5185185e-02, 3.0570902e-01 165 | 7.9629630e-02, 2.9465930e-01 166 | 9.2592593e-02, 2.9097606e-01 167 | 1.1481481e-01, 2.7071823e-01 168 | 1.1666667e-01, 2.5598527e-01 169 | 9.4444444e-02, 2.3941068e-01 170 | 1.0000000e-01, 2.2651934e-01 171 | 8.8888889e-02, 2.1915285e-01 172 | 8.7037037e-02, 2.0994475e-01 173 | 2.4814815e-01, 2.0441989e-01 174 | 5.9259259e-02, 2.0257827e-01 175 | 4.4444444e-02, 2.0073665e-01 176 | 7.5925926e-02, 2.0073665e-01 177 | 2.8148148e-01, 1.9705341e-01 178 | 2.4444444e-01, 1.9152855e-01 179 | 5.5555556e-02, 1.8784530e-01 180 | 2.6851852e-01, 1.8047882e-01 181 | 2.5185185e-01, 1.7679558e-01 182 | 2.3148148e-01, 1.6022099e-01 183 | 1.2592593e-01, 1.3627993e-01 184 | 2.3703704e-01, 1.2154696e-01 185 | 2.1481481e-01, 1.2154696e-01 186 | 2.3148148e-01, 1.1049724e-01 187 | 1.0185185e-01, 9.9447514e-02 188 | 2.0740741e-01, 9.5764273e-02 189 | 2.2962963e-01, 9.2081031e-02 190 | 1.0185185e-01, 8.8397790e-02 191 | 1.6111111e-01, 7.5506446e-02 192 | 1.7407407e-01, 7.5506446e-02 193 | 1.2592593e-01, 7.3664825e-02 194 | 1.3888889e-01, 6.6298343e-02 195 | 1.9074074e-01, 6.6298343e-02 196 | -------------------------------------------------------------------------------- /errata.md: -------------------------------------------------------------------------------- 1 | # Errata 2 | 3 | ## Chapter 2 4 | 5 | page 50 6 | 7 | - As we can see, the result looks somewhat similar for lossf_a is $\hat \theta = 0.32$ 8 | 9 | page 59: 10 | 11 | - second paragraph: "I(t')s the theoretical distribution [...]" 12 | - last paragraph, third sentence: replace "for $\nu > 2$" with "for $\nu <= 2$" 13 | 14 | page 63: 15 | 16 | - second paragraph: "how a few of the predictive samples look[s] very flat." 17 | 18 | page 64: 19 | 20 | - first paragraph: "to the value[s] estimated" 21 | 22 | page 65: 23 | 24 | - second paragraph: "help their kids grow[n] stronger" 25 | 26 | page 66: 27 | 28 | - the pooled standard deviation should have a plus (+) instead of a minus (-) sign between the group standard deviations. That is: 29 | $$\frac{\mu_2 - \mu_1}{\sqrt{\frac{{\sigma_2}^2 + {\sigma_1}^2}{2}}}$$ 30 | 31 | page 73: 32 | 33 | - first paragraph: "i(t')s also possible" 34 | 35 | page 83: 36 | 37 | - first paragraph: "And that is, ladies, gentlem[a](e)n" 38 | 39 | 40 | ## Chapter 3 41 | 42 | page 95: 43 | 44 | - first paragraph: "this constrain(t) is relaxed" 45 | 46 | page 100: 47 | 48 | - second to last paragraph: "interval [-1, 1][. It does not matter about](, regardless of) the scale of the data." 49 | - last paragraph: "how much y change(s)" 50 | 51 | page 109: 52 | 53 | - code block, 4th and 3rd line from bottom: f-strings missing the "f". 54 | 55 | page 116: 56 | 57 | - first paragraph: "with an[d] increasing amount" 58 | 59 | page 119: 60 | 61 | - second paragraph: "They are just (k)nobs" 62 | 63 | page 120: 64 | 65 | - first paragraph: "Well that's the subject of Chapter [6] (5), Model Comparison" 66 | 67 | page 121: 68 | 69 | Here, $\beta$ is a vector of coefficients of length $m$, that is, the number of [in]dependent variables. 70 | 71 | page 128: 72 | 73 | - first paragraph: "Using a fo(rest plot)" 74 | 75 | page 133: 76 | 77 | - Figure 3.26 have been updated 78 | 79 | page 135: 80 | 81 | - In all of the examples we have seen so far, the [in]dependent variables contribute additively to the predicted variable. 82 | 83 | page 136: 84 | 85 | - For those cases, we may want to consider the variance as a (linear) function of the [in]dependent variable. 86 | 87 | Page 141: 88 | 89 | - Exercise 6: "ArviZ functions like plot_trace and plot_pair" 90 | 91 | Page 144: 92 | - Exercise 14: This time [a]dd uncertainy to the linear plot 93 | - Exercise 14: Exercise should reference Figure 3.17, not Figure 3.18 94 | 95 | ## Chapter 4 96 | 97 | page 149: 98 | 99 | - first paragraph: "scatter[s] plots" 100 | 101 | page 154: 102 | 103 | - second paragraph: "we take advantage[s]" 104 | 105 | page 160: 106 | 107 | - third paragraph: "and 50 virgini[n](c)as" 108 | 109 | page 163: 110 | 111 | - bottom paragraph: "Chapter 5, [Modeling with Linear Regression] (Model Comparison)" 112 | 113 | page 167: 114 | 115 | - last line: "x! is the factorial of [k ...]" - replace k with x everywhere in the following expression. 116 | 117 | page 170: 118 | 119 | - equation (4.25): $$p(y_j = k_i) = \psi \frac{\mu^{x_i}e^{-\mu}}{x_i!}$$ 120 | 121 | page 175: 122 | 123 | - "Extensions such (as) the ones we [we] saw" 124 | 125 | 126 | ## Chapter 5 127 | 128 | page 185: 129 | 130 | - first paragraph: "shocked or even disappoint[ing] (ed) by" 131 | 132 | page 221: 133 | 134 | - Bayes factors are problematic to use, given that they are very sensitive[ly] to prior specification, 135 | 136 | 137 | ## Chapter 6 138 | 139 | page 230: 140 | 141 | - last paragraph: "does not necessary depend[s] on data" 142 | 143 | Page 218: 144 | 145 | - "KL diverge[nce] is useful because it is a way of measuring how close to distributins are 146 | 147 | 148 | page 237: 149 | 150 | - third paragraph: "logistic[s] regression" 151 | 152 | 153 | page 238: 154 | 155 | - first paragraph: "someone already decide(d) the name" 156 | 157 | 158 | page 239: 159 | 160 | - third paragraph: "br[ake] (eak) the stick" 161 | 162 | 163 | page 241: 164 | 165 | - first paragraph: "represent(s) how confiden[ce] (t) we are" 166 | 167 | 168 | page 245: 169 | 170 | - first paragraph: "This model also show(s) a less smooth" 171 | 172 | 173 | page 247: 174 | 175 | - last paragraph: "wi[n]ch may lead to" 176 | 177 | 178 | page 248: 179 | 180 | - second paragraph: 181 | + "thus i[n] (t) may be convenient" 182 | + "th[is] (ese) models can lead" 183 | 184 | - last paragraph: "can be interpreted as continuous mixture model(s)" 185 | 186 | 187 | ## Chapter 7 188 | 189 | page 253: 190 | 191 | - last paragraph: 192 | + "we express the first [one] function" 193 | + "for the second [one] function" 194 | 195 | page 255: 196 | 197 | - equation 7.4: second term on the RHS should be: $(x_2 - x_2')^2$ 198 | - third paragraph: "covariance matrix looks [appears] for different inputs" 199 | - info box: "Thus, the close[st] (r) two points are on the x axis[;] (,) the mo[st] (re) similar we expect their values to be on the y axis" 200 | 201 | 202 | page 259: 203 | 204 | - last paragraph: "and [this is not the exception with] Gaussian processes (are no exception)" 205 | 206 | page 261: 207 | 208 | - second paragraph: "$x$ is the independent variable[s], and $y$ is the dependent variable[s]" 209 | - third paragraph: "module, [Often, ]for length-scale parameters, priors avoiding zero (often) work better" 210 | 211 | page 264: 212 | 213 | - first paragraph: "their geographical similar[ly] (ity)" 214 | 215 | page 271: 216 | 217 | - second paragraph: "counteracting the effect of it('s)[ over] close neighbors" 218 | 219 | page 279: 220 | 221 | - last paragraph: "to the time a disaster[s] happen[s] (ed)" 222 | 223 | 224 | page 280: 225 | 226 | - first paragraph: "Let's load [at] the data" 227 | 228 | page 285: 229 | 230 | - last paragraph: "We may imag[e] (ine) that" 231 | 232 | 233 | ## Chapter 8 234 | 235 | page 295: 236 | 237 | - second paragraph: "[Also is] (It's also) one of the building block(s)" 238 | 239 | page 300: 240 | 241 | - first paragraph: "Monte Carlo is a very famous casino located in the Principality of Monaco." could be replaced with the factually correct: "Monte Carlo is a ward in the Principality of Monaco where a very famous casino is located". 242 | 243 | page 303: 244 | 245 | - third paragraph: "The rule to decide whether to accept or reject is known as the Metropolis criteri[a] (on)" 246 | 247 | page 318: 248 | 249 | - second paragraph: "samples from the noncentered model ha[s] (ve) almost no autocorrelation" 250 | 251 | page 319: 252 | 253 | - second paragraph: " we will need a [more] (larger) effective sample size" 254 | 255 | page 327: 256 | 257 | - first paragraph: 258 | + "One book that is generally refere[e]d (to) as" 259 | + "You may also want to check (out) the book" 260 | 261 | 262 | -------------------------------------------------------------------------------- /code/data/tips.csv: -------------------------------------------------------------------------------- 1 | total_bill,tip,sex,smoker,day,time,size 2 | 16.99,1.01,Female,No,Sun,Dinner,2 3 | 10.34,1.66,Male,No,Sun,Dinner,3 4 | 21.01,3.5,Male,No,Sun,Dinner,3 5 | 23.68,3.31,Male,No,Sun,Dinner,2 6 | 24.59,3.61,Female,No,Sun,Dinner,4 7 | 25.29,4.71,Male,No,Sun,Dinner,4 8 | 8.77,2.0,Male,No,Sun,Dinner,2 9 | 26.88,3.12,Male,No,Sun,Dinner,4 10 | 15.04,1.96,Male,No,Sun,Dinner,2 11 | 14.78,3.23,Male,No,Sun,Dinner,2 12 | 10.27,1.71,Male,No,Sun,Dinner,2 13 | 35.26,5.0,Female,No,Sun,Dinner,4 14 | 15.42,1.57,Male,No,Sun,Dinner,2 15 | 18.43,3.0,Male,No,Sun,Dinner,4 16 | 14.83,3.02,Female,No,Sun,Dinner,2 17 | 21.58,3.92,Male,No,Sun,Dinner,2 18 | 10.33,1.67,Female,No,Sun,Dinner,3 19 | 16.29,3.71,Male,No,Sun,Dinner,3 20 | 16.97,3.5,Female,No,Sun,Dinner,3 21 | 20.65,3.35,Male,No,Sat,Dinner,3 22 | 17.92,4.08,Male,No,Sat,Dinner,2 23 | 20.29,2.75,Female,No,Sat,Dinner,2 24 | 15.77,2.23,Female,No,Sat,Dinner,2 25 | 39.42,7.58,Male,No,Sat,Dinner,4 26 | 19.82,3.18,Male,No,Sat,Dinner,2 27 | 17.81,2.34,Male,No,Sat,Dinner,4 28 | 13.37,2.0,Male,No,Sat,Dinner,2 29 | 12.69,2.0,Male,No,Sat,Dinner,2 30 | 21.7,4.3,Male,No,Sat,Dinner,2 31 | 19.65,3.0,Female,No,Sat,Dinner,2 32 | 9.55,1.45,Male,No,Sat,Dinner,2 33 | 18.35,2.5,Male,No,Sat,Dinner,4 34 | 15.06,3.0,Female,No,Sat,Dinner,2 35 | 20.69,2.45,Female,No,Sat,Dinner,4 36 | 17.78,3.27,Male,No,Sat,Dinner,2 37 | 24.06,3.6,Male,No,Sat,Dinner,3 38 | 16.31,2.0,Male,No,Sat,Dinner,3 39 | 16.93,3.07,Female,No,Sat,Dinner,3 40 | 18.69,2.31,Male,No,Sat,Dinner,3 41 | 31.27,5.0,Male,No,Sat,Dinner,3 42 | 16.04,2.24,Male,No,Sat,Dinner,3 43 | 17.46,2.54,Male,No,Sun,Dinner,2 44 | 13.94,3.06,Male,No,Sun,Dinner,2 45 | 9.68,1.32,Male,No,Sun,Dinner,2 46 | 30.4,5.6,Male,No,Sun,Dinner,4 47 | 18.29,3.0,Male,No,Sun,Dinner,2 48 | 22.23,5.0,Male,No,Sun,Dinner,2 49 | 32.4,6.0,Male,No,Sun,Dinner,4 50 | 28.55,2.05,Male,No,Sun,Dinner,3 51 | 18.04,3.0,Male,No,Sun,Dinner,2 52 | 12.54,2.5,Male,No,Sun,Dinner,2 53 | 10.29,2.6,Female,No,Sun,Dinner,2 54 | 34.81,5.2,Female,No,Sun,Dinner,4 55 | 9.94,1.56,Male,No,Sun,Dinner,2 56 | 25.56,4.34,Male,No,Sun,Dinner,4 57 | 19.49,3.51,Male,No,Sun,Dinner,2 58 | 38.01,3.0,Male,Yes,Sat,Dinner,4 59 | 26.41,1.5,Female,No,Sat,Dinner,2 60 | 11.24,1.76,Male,Yes,Sat,Dinner,2 61 | 48.27,6.73,Male,No,Sat,Dinner,4 62 | 20.29,3.21,Male,Yes,Sat,Dinner,2 63 | 13.81,2.0,Male,Yes,Sat,Dinner,2 64 | 11.02,1.98,Male,Yes,Sat,Dinner,2 65 | 18.29,3.76,Male,Yes,Sat,Dinner,4 66 | 17.59,2.64,Male,No,Sat,Dinner,3 67 | 20.08,3.15,Male,No,Sat,Dinner,3 68 | 16.45,2.47,Female,No,Sat,Dinner,2 69 | 3.07,1.0,Female,Yes,Sat,Dinner,1 70 | 20.23,2.01,Male,No,Sat,Dinner,2 71 | 15.01,2.09,Male,Yes,Sat,Dinner,2 72 | 12.02,1.97,Male,No,Sat,Dinner,2 73 | 17.07,3.0,Female,No,Sat,Dinner,3 74 | 26.86,3.14,Female,Yes,Sat,Dinner,2 75 | 25.28,5.0,Female,Yes,Sat,Dinner,2 76 | 14.73,2.2,Female,No,Sat,Dinner,2 77 | 10.51,1.25,Male,No,Sat,Dinner,2 78 | 17.92,3.08,Male,Yes,Sat,Dinner,2 79 | 27.2,4.0,Male,No,Thur,Lunch,4 80 | 22.76,3.0,Male,No,Thur,Lunch,2 81 | 17.29,2.71,Male,No,Thur,Lunch,2 82 | 19.44,3.0,Male,Yes,Thur,Lunch,2 83 | 16.66,3.4,Male,No,Thur,Lunch,2 84 | 10.07,1.83,Female,No,Thur,Lunch,1 85 | 32.68,5.0,Male,Yes,Thur,Lunch,2 86 | 15.98,2.03,Male,No,Thur,Lunch,2 87 | 34.83,5.17,Female,No,Thur,Lunch,4 88 | 13.03,2.0,Male,No,Thur,Lunch,2 89 | 18.28,4.0,Male,No,Thur,Lunch,2 90 | 24.71,5.85,Male,No,Thur,Lunch,2 91 | 21.16,3.0,Male,No,Thur,Lunch,2 92 | 28.97,3.0,Male,Yes,Fri,Dinner,2 93 | 22.49,3.5,Male,No,Fri,Dinner,2 94 | 5.75,1.0,Female,Yes,Fri,Dinner,2 95 | 16.32,4.3,Female,Yes,Fri,Dinner,2 96 | 22.75,3.25,Female,No,Fri,Dinner,2 97 | 40.17,4.73,Male,Yes,Fri,Dinner,4 98 | 27.28,4.0,Male,Yes,Fri,Dinner,2 99 | 12.03,1.5,Male,Yes,Fri,Dinner,2 100 | 21.01,3.0,Male,Yes,Fri,Dinner,2 101 | 12.46,1.5,Male,No,Fri,Dinner,2 102 | 11.35,2.5,Female,Yes,Fri,Dinner,2 103 | 15.38,3.0,Female,Yes,Fri,Dinner,2 104 | 44.3,2.5,Female,Yes,Sat,Dinner,3 105 | 22.42,3.48,Female,Yes,Sat,Dinner,2 106 | 20.92,4.08,Female,No,Sat,Dinner,2 107 | 15.36,1.64,Male,Yes,Sat,Dinner,2 108 | 20.49,4.06,Male,Yes,Sat,Dinner,2 109 | 25.21,4.29,Male,Yes,Sat,Dinner,2 110 | 18.24,3.76,Male,No,Sat,Dinner,2 111 | 14.31,4.0,Female,Yes,Sat,Dinner,2 112 | 14.0,3.0,Male,No,Sat,Dinner,2 113 | 7.25,1.0,Female,No,Sat,Dinner,1 114 | 38.07,4.0,Male,No,Sun,Dinner,3 115 | 23.95,2.55,Male,No,Sun,Dinner,2 116 | 25.71,4.0,Female,No,Sun,Dinner,3 117 | 17.31,3.5,Female,No,Sun,Dinner,2 118 | 29.93,5.07,Male,No,Sun,Dinner,4 119 | 10.65,1.5,Female,No,Thur,Lunch,2 120 | 12.43,1.8,Female,No,Thur,Lunch,2 121 | 24.08,2.92,Female,No,Thur,Lunch,4 122 | 11.69,2.31,Male,No,Thur,Lunch,2 123 | 13.42,1.68,Female,No,Thur,Lunch,2 124 | 14.26,2.5,Male,No,Thur,Lunch,2 125 | 15.95,2.0,Male,No,Thur,Lunch,2 126 | 12.48,2.52,Female,No,Thur,Lunch,2 127 | 29.8,4.2,Female,No,Thur,Lunch,6 128 | 8.52,1.48,Male,No,Thur,Lunch,2 129 | 14.52,2.0,Female,No,Thur,Lunch,2 130 | 11.38,2.0,Female,No,Thur,Lunch,2 131 | 22.82,2.18,Male,No,Thur,Lunch,3 132 | 19.08,1.5,Male,No,Thur,Lunch,2 133 | 20.27,2.83,Female,No,Thur,Lunch,2 134 | 11.17,1.5,Female,No,Thur,Lunch,2 135 | 12.26,2.0,Female,No,Thur,Lunch,2 136 | 18.26,3.25,Female,No,Thur,Lunch,2 137 | 8.51,1.25,Female,No,Thur,Lunch,2 138 | 10.33,2.0,Female,No,Thur,Lunch,2 139 | 14.15,2.0,Female,No,Thur,Lunch,2 140 | 16.0,2.0,Male,Yes,Thur,Lunch,2 141 | 13.16,2.75,Female,No,Thur,Lunch,2 142 | 17.47,3.5,Female,No,Thur,Lunch,2 143 | 34.3,6.7,Male,No,Thur,Lunch,6 144 | 41.19,5.0,Male,No,Thur,Lunch,5 145 | 27.05,5.0,Female,No,Thur,Lunch,6 146 | 16.43,2.3,Female,No,Thur,Lunch,2 147 | 8.35,1.5,Female,No,Thur,Lunch,2 148 | 18.64,1.36,Female,No,Thur,Lunch,3 149 | 11.87,1.63,Female,No,Thur,Lunch,2 150 | 9.78,1.73,Male,No,Thur,Lunch,2 151 | 7.51,2.0,Male,No,Thur,Lunch,2 152 | 14.07,2.5,Male,No,Sun,Dinner,2 153 | 13.13,2.0,Male,No,Sun,Dinner,2 154 | 17.26,2.74,Male,No,Sun,Dinner,3 155 | 24.55,2.0,Male,No,Sun,Dinner,4 156 | 19.77,2.0,Male,No,Sun,Dinner,4 157 | 29.85,5.14,Female,No,Sun,Dinner,5 158 | 48.17,5.0,Male,No,Sun,Dinner,6 159 | 25.0,3.75,Female,No,Sun,Dinner,4 160 | 13.39,2.61,Female,No,Sun,Dinner,2 161 | 16.49,2.0,Male,No,Sun,Dinner,4 162 | 21.5,3.5,Male,No,Sun,Dinner,4 163 | 12.66,2.5,Male,No,Sun,Dinner,2 164 | 16.21,2.0,Female,No,Sun,Dinner,3 165 | 13.81,2.0,Male,No,Sun,Dinner,2 166 | 17.51,3.0,Female,Yes,Sun,Dinner,2 167 | 24.52,3.48,Male,No,Sun,Dinner,3 168 | 20.76,2.24,Male,No,Sun,Dinner,2 169 | 31.71,4.5,Male,No,Sun,Dinner,4 170 | 10.59,1.61,Female,Yes,Sat,Dinner,2 171 | 10.63,2.0,Female,Yes,Sat,Dinner,2 172 | 50.81,10.0,Male,Yes,Sat,Dinner,3 173 | 15.81,3.16,Male,Yes,Sat,Dinner,2 174 | 7.25,5.15,Male,Yes,Sun,Dinner,2 175 | 31.85,3.18,Male,Yes,Sun,Dinner,2 176 | 16.82,4.0,Male,Yes,Sun,Dinner,2 177 | 32.9,3.11,Male,Yes,Sun,Dinner,2 178 | 17.89,2.0,Male,Yes,Sun,Dinner,2 179 | 14.48,2.0,Male,Yes,Sun,Dinner,2 180 | 9.6,4.0,Female,Yes,Sun,Dinner,2 181 | 34.63,3.55,Male,Yes,Sun,Dinner,2 182 | 34.65,3.68,Male,Yes,Sun,Dinner,4 183 | 23.33,5.65,Male,Yes,Sun,Dinner,2 184 | 45.35,3.5,Male,Yes,Sun,Dinner,3 185 | 23.17,6.5,Male,Yes,Sun,Dinner,4 186 | 40.55,3.0,Male,Yes,Sun,Dinner,2 187 | 20.69,5.0,Male,No,Sun,Dinner,5 188 | 20.9,3.5,Female,Yes,Sun,Dinner,3 189 | 30.46,2.0,Male,Yes,Sun,Dinner,5 190 | 18.15,3.5,Female,Yes,Sun,Dinner,3 191 | 23.1,4.0,Male,Yes,Sun,Dinner,3 192 | 15.69,1.5,Male,Yes,Sun,Dinner,2 193 | 19.81,4.19,Female,Yes,Thur,Lunch,2 194 | 28.44,2.56,Male,Yes,Thur,Lunch,2 195 | 15.48,2.02,Male,Yes,Thur,Lunch,2 196 | 16.58,4.0,Male,Yes,Thur,Lunch,2 197 | 7.56,1.44,Male,No,Thur,Lunch,2 198 | 10.34,2.0,Male,Yes,Thur,Lunch,2 199 | 43.11,5.0,Female,Yes,Thur,Lunch,4 200 | 13.0,2.0,Female,Yes,Thur,Lunch,2 201 | 13.51,2.0,Male,Yes,Thur,Lunch,2 202 | 18.71,4.0,Male,Yes,Thur,Lunch,3 203 | 12.74,2.01,Female,Yes,Thur,Lunch,2 204 | 13.0,2.0,Female,Yes,Thur,Lunch,2 205 | 16.4,2.5,Female,Yes,Thur,Lunch,2 206 | 20.53,4.0,Male,Yes,Thur,Lunch,4 207 | 16.47,3.23,Female,Yes,Thur,Lunch,3 208 | 26.59,3.41,Male,Yes,Sat,Dinner,3 209 | 38.73,3.0,Male,Yes,Sat,Dinner,4 210 | 24.27,2.03,Male,Yes,Sat,Dinner,2 211 | 12.76,2.23,Female,Yes,Sat,Dinner,2 212 | 30.06,2.0,Male,Yes,Sat,Dinner,3 213 | 25.89,5.16,Male,Yes,Sat,Dinner,4 214 | 48.33,9.0,Male,No,Sat,Dinner,4 215 | 13.27,2.5,Female,Yes,Sat,Dinner,2 216 | 28.17,6.5,Female,Yes,Sat,Dinner,3 217 | 12.9,1.1,Female,Yes,Sat,Dinner,2 218 | 28.15,3.0,Male,Yes,Sat,Dinner,5 219 | 11.59,1.5,Male,Yes,Sat,Dinner,2 220 | 7.74,1.44,Male,Yes,Sat,Dinner,2 221 | 30.14,3.09,Female,Yes,Sat,Dinner,4 222 | 12.16,2.2,Male,Yes,Fri,Lunch,2 223 | 13.42,3.48,Female,Yes,Fri,Lunch,2 224 | 8.58,1.92,Male,Yes,Fri,Lunch,1 225 | 15.98,3.0,Female,No,Fri,Lunch,3 226 | 13.42,1.58,Male,Yes,Fri,Lunch,2 227 | 16.27,2.5,Female,Yes,Fri,Lunch,2 228 | 10.09,2.0,Female,Yes,Fri,Lunch,2 229 | 20.45,3.0,Male,No,Sat,Dinner,4 230 | 13.28,2.72,Male,No,Sat,Dinner,2 231 | 22.12,2.88,Female,Yes,Sat,Dinner,2 232 | 24.01,2.0,Male,Yes,Sat,Dinner,4 233 | 15.69,3.0,Male,Yes,Sat,Dinner,3 234 | 11.61,3.39,Male,No,Sat,Dinner,2 235 | 10.77,1.47,Male,No,Sat,Dinner,2 236 | 15.53,3.0,Male,Yes,Sat,Dinner,2 237 | 10.07,1.25,Male,No,Sat,Dinner,2 238 | 12.6,1.0,Male,Yes,Sat,Dinner,2 239 | 32.83,1.17,Male,Yes,Sat,Dinner,2 240 | 35.83,4.67,Female,No,Sat,Dinner,3 241 | 29.03,5.92,Male,No,Sat,Dinner,3 242 | 27.18,2.0,Female,Yes,Sat,Dinner,2 243 | 22.67,2.0,Male,Yes,Sat,Dinner,2 244 | 17.82,1.75,Male,No,Sat,Dinner,2 245 | 18.78,3.0,Female,No,Thur,Dinner,2 246 | -------------------------------------------------------------------------------- /code/data/babies.csv: -------------------------------------------------------------------------------- 1 | Month,Lenght 2 | 0,48.5 3 | 0,50.5 4 | 0,50.5 5 | 0,52.0 6 | 0,47.5 7 | 0,49.0 8 | 0,45.5 9 | 0,48.5 10 | 0,50.0 11 | 0,47.5 12 | 0,49.5 13 | 0,49.0 14 | 0,49.5 15 | 0,48.5 16 | 0,48.5 17 | 0,49.0 18 | 0,50.0 19 | 0,48.0 20 | 0,49.5 21 | 0,49.5 22 | 0,51.5 23 | 0,49.0 24 | 0,48.5 25 | 0,53.0 26 | 0,45.5 27 | 0,51.5 28 | 0,51.0 29 | 0,47.5 30 | 0,51.5 31 | 0,50.0 32 | 0,48.5 33 | 0,47.0 34 | 0,50.5 35 | 0,48.0 36 | 0,47.0 37 | 0,48.5 38 | 0,48.5 39 | 0,49.5 40 | 0,49.0 41 | 0,51.5 42 | 0,50.5 43 | 0,49.0 44 | 0,47.5 45 | 0,51.5 46 | 0,51.0 47 | 0,54.5 48 | 0,50.0 49 | 0,52.5 50 | 1,51.5 51 | 1,52.0 52 | 1,51.5 53 | 1,54.0 54 | 1,55.0 55 | 1,55.5 56 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| 106.68;15.989118;8;0 251 | 146.685;36.0889135;62;0 252 | 152.4;40.879979;67;0 253 | 163.83;47.910655;57;1 254 | 165.735;47.7122085;32;1 255 | 156.21;46.379782;24;0 256 | 152.4;41.163474;77;1 257 | 140.335;36.5992045;62;0 258 | 158.115;43.09124;17;1 259 | 163.195;48.137451;67;1 260 | 151.13;36.7126025;70;0 261 | 171.1198;56.5572525;37;1 262 | 149.86;38.6970675;58;0 263 | 163.83;47.4854125;35;1 264 | 141.605;36.2023115;30;0 265 | 93.98;14.288148;5;0 266 | 149.225;41.276872;26;0 267 | 105.41;15.2236815;5;0 268 | 146.05;44.7638605;21;0 269 | 161.29;50.4337605;41;1 270 | 162.56;55.281525;46;1 271 | 145.415;37.931631;49;0 272 | 145.415;35.493574;15;1 273 | 170.815;58.456669;28;1 274 | 127;21.488921;12;0 275 | 159.385;44.4236665;83;0 276 | 159.4;44.4;54;1 277 | 153.67;44.565414;54;0 278 | 160.02;44.622113;68;1 279 | 150.495;40.483086;68;0 280 | 149.225;44.0834725;56;0 281 | 127;24.4089195;15;0 282 | 142.875;34.416293;57;0 283 | 142.113;32.772022;22;0 284 | 147.32;35.947166;40;0 285 | 162.56;49.5549;19;1 286 | 164.465;53.183662;41;1 287 | 160.02;37.081146;75.9000000000001;1 288 | 153.67;40.5114355;73.9000000000001;0 289 | 167.005;50.6038575;49;1 290 | 151.13;43.9700745;26;1 291 | 147.955;33.792604;17;0 292 | 125.3998;21.375523;13;0 293 | 111.125;16.669506;8;0 294 | 153.035;49.89;88;1 295 | 139.065;33.5941575;68;0 296 | 152.4;43.8566765;33;1 297 | 154.94;48.137451;26;0 298 | 147.955;42.751046;56;0 299 | 143.51;34.8415355;16;1 300 | 117.983;24.097075;13;0 301 | 144.145;33.906002;34;0 302 | 92.71;12.076887;5;0 303 | 147.955;41.276872;17;0 304 | 155.575;39.7176495;74;1 305 | 150.495;35.947166;69;0 306 | 155.575;50.915702;50;1 307 | 154.305;45.756093;44;0 308 | 130.6068;25.2594045;15;0 309 | 101.6;15.3370795;5;0 310 | 157.48;49.214732;18;0 311 | 168.91;58.8252125;41;1 312 | 150.495;43.4597835;27;0 313 | 111.76;17.8318355;8.90000000000009;1 314 | 160.02;51.9646335;38;1 315 | 167.64;50.688906;57;1 316 | 144.145;34.246196;64.5;0 317 | 145.415;39.3774555;42;0 318 | 160.02;59.5622995;24;1 319 | 147.32;40.312989;16;1 320 | 164.465;52.16308;71;1 321 | 153.035;39.972795;49.5;0 322 | 149.225;43.941725;33;1 323 | 160.02;54.601137;28;0 324 | 149.225;45.075705;47;0 325 | 85.09;11.453198;3;1 326 | 84.455;11.7650425;1;1 327 | 59.6138;5.896696;1;0 328 | 92.71;12.1052365;3;1 329 | 111.125;18.313777;6;0 330 | 90.805;11.3681495;5;0 331 | 153.67;41.333571;27;0 332 | 99.695;16.2442635;5;0 333 | 62.484;6.80388;1;0 334 | 81.915;11.8784405;2;1 335 | 96.52;14.968536;2;0 336 | 80.01;9.865626;1;1 337 | 150.495;41.900561;55;0 338 | 151.765;42.524;83.4000000000001;1 339 | 140.6398;28.859791;12;1 340 | 88.265;12.7856245;2;0 341 | 158.115;43.147939;63;1 342 | 149.225;40.82328;52;0 343 | 151.765;42.864444;49;1 344 | 154.94;46.209685;31;0 345 | 123.825;20.581737;9;0 346 | 104.14;15.87572;6;0 347 | 161.29;47.853956;35;1 348 | 148.59;42.52425;35;0 349 | 97.155;17.066399;7;0 350 | 93.345;13.1825175;5;1 351 | 160.655;48.5059945;24;1 352 | 157.48;45.869491;41;1 353 | 167.005;52.900167;32;1 354 | 157.48;47.570461;43;1 355 | 91.44;12.927372;6;0 356 | 60.452;5.6699;1;1 357 | 137.16;28.91649;15;1 358 | 152.4;43.544832;63;0 359 | 152.4;43.431434;21;0 360 | 81.28;11.509897;1;1 361 | 109.22;11.7083435;2;0 362 | 71.12;7.540967;1;1 363 | 89.2048;12.700576;3;0 364 | 67.31;7.200773;1;0 365 | 85.09;12.360382;1;1 366 | 69.85;7.7961125;1;0 367 | 161.925;53.2120115;55;0 368 | 152.4;44.678812;38;0 369 | 88.9;12.5588285;3;1 370 | 90.17;12.700576;3;1 371 | 71.755;7.37087;1;0 372 | 83.82;9.2135875;1;0 373 | 159.385;47.2019175;28;1 374 | 142.24;28.632995;16;0 375 | 142.24;31.6663915;36;0 376 | 168.91;56.4438545;38;1 377 | 123.19;20.014747;12;1 378 | 74.93;8.50485;1;1 379 | 74.295;8.3064035;1;0 380 | 90.805;11.623295;3;0 381 | 160.02;55.791816;48;1 382 | 67.945;7.9662095;1;0 383 | 135.89;27.21552;15;0 384 | 158.115;47.4854125;45;1 385 | 85.09;10.8011595;3;1 386 | 93.345;14.004653;3;0 387 | 152.4;45.1607535;38;0 388 | 155.575;45.529297;21;0 389 | 154.305;48.874538;50;0 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158.75;48.6760915;28;1 426 | 142.875;35.606972;42;0 427 | 84.455;9.3836845;2;1 428 | 151.9428;43.714929;21;1 429 | 161.29;48.19415;19;1 430 | 127.9906;29.8520235;13;1 431 | 160.9852;50.972401;48;1 432 | 144.78;43.998424;46;0 433 | 132.08;28.292801;11;1 434 | 117.983;20.354941;8;1 435 | 160.02;48.19415;25;1 436 | 154.94;39.179009;16;1 437 | 160.9852;46.6916265;51;1 438 | 165.989;56.415505;25;1 439 | 157.988;48.591043;28;1 440 | 154.94;48.2224995;26;0 441 | 97.9932;13.2959155;5;1 442 | 64.135;6.6621325;1;0 443 | 160.655;47.4854125;54;1 444 | 147.32;35.550273;66;0 445 | 146.7;36.6;20;0 446 | 147.32;48.9595865;25;0 447 | 172.9994;51.255896;38;1 448 | 158.115;46.5215295;51;1 449 | 147.32;36.967748;48;0 450 | 124.9934;25.117657;13;1 451 | 106.045;16.272613;6;1 452 | 165.989;48.647742;27;1 453 | 149.86;38.045029;22;0 454 | 76.2;8.50485;1;0 455 | 161.925;47.286966;60;1 456 | 140.0048;28.3495;15;0 457 | 66.675;8.1363065;0;0 458 | 62.865;7.200773;0;1 459 | 163.83;55.394923;43;1 460 | 147.955;32.488527;12;1 461 | 160.02;54.204244;27;1 462 | 154.94;48.477645;30;1 463 | 152.4;43.0628905;29;0 464 | 62.23;7.257472;0;0 465 | 146.05;34.189497;23;0 466 | 151.9936;49.951819;30;0 467 | 157.48;41.3052215;17;1 468 | 55.88;4.8477645;0;0 469 | 60.96;6.23689;0;1 470 | 151.765;44.338618;41;0 471 | 144.78;33.45241;42;0 472 | 118.11;16.896302;7;0 473 | 78.105;8.221355;3;0 474 | 160.655;47.286966;43;1 475 | 151.13;46.1246365;35;0 476 | 121.92;20.184844;10;0 477 | 92.71;12.757275;3;1 478 | 153.67;47.400364;75.5;1 479 | 147.32;40.8516295;64;0 480 | 139.7;50.348712;38;1 481 | 157.48;45.132404;24.2;0 482 | 91.44;11.623295;4;0 483 | 154.94;42.240755;26;1 484 | 143.51;41.6454155;19;0 485 | 83.185;9.1568885;2;1 486 | 158.115;45.2174525;43;1 487 | 147.32;51.255896;38;0 488 | 123.825;21.205426;10;1 489 | 88.9;11.5949455;3;1 490 | 160.02;49.271431;23;1 491 | 137.16;27.952607;16;0 492 | 165.1;51.199197;49;1 493 | 154.94;43.8566765;41;0 494 | 111.125;17.690088;6;1 495 | 153.67;35.5219235;23;0 496 | 145.415;34.246196;14;0 497 | 141.605;42.88542;43;0 498 | 144.78;32.545226;15;0 499 | 163.83;46.776675;21;1 500 | 161.29;41.8722115;24;1 501 | 154.9;38.2;20;1 502 | 161.3;43.3;20;1 503 | 170.18;53.637254;34;1 504 | 149.86;42.977842;29;0 505 | 123.825;21.54562;11;1 506 | 85.09;11.4248485;3;0 507 | 160.655;39.7743485;65;1 508 | 154.94;43.3463855;46;0 509 | 106.045;15.478827;8;0 510 | 126.365;21.9141635;15;1 511 | 166.37;52.673371;43;1 512 | 148.2852;38.441922;39;0 513 | 124.46;19.27766;12;0 514 | 89.535;11.113004;3;1 515 | 101.6;13.494362;4;0 516 | 151.765;42.807745;43;0 517 | 148.59;35.890467;70;0 518 | 153.67;44.22522;26;0 519 | 53.975;4.252425;0;0 520 | 146.685;38.0733785;48;0 521 | 56.515;5.159609;0;0 522 | 100.965;14.3164975;5;1 523 | 121.92;23.2182405;8;1 524 | 81.5848;10.659412;3;0 525 | 154.94;44.111822;44;1 526 | 156.21;44.0267735;33;0 527 | 132.715;24.9759095;15;1 528 | 125.095;22.5945515;12;0 529 | 101.6;14.344847;5;0 530 | 160.655;47.8823055;41;1 531 | 146.05;39.405805;37.4;0 532 | 132.715;24.777463;13;0 533 | 87.63;10.659412;6;0 534 | 156.21;41.050076;53;1 535 | 152.4;40.82328;49;0 536 | 162.56;47.0318205;27;0 537 | 114.935;17.519991;7;1 538 | 67.945;7.2291225;1;0 539 | 142.875;34.246196;31;0 540 | 76.835;8.0229085;1;1 541 | 145.415;31.127751;17;1 542 | 162.56;52.16308;31;1 543 | 156.21;54.0624965;21;0 544 | 71.12;8.051258;0;1 545 | 158.75;52.5316235;68;1 546 | -------------------------------------------------------------------------------- /extras/concrete.csv: -------------------------------------------------------------------------------- 1 | CCS,cement,water 2 | 79.99,540.0,162.0 3 | 61.89,540.0,162.0 4 | 40.27,332.5,228.0 5 | 41.05,332.5,228.0 6 | 44.30,198.6,192.0 7 | 47.03,266.0,228.0 8 | 43.70,380.0,228.0 9 | 36.45,380.0,228.0 10 | 45.85,266.0,228.0 11 | 39.29,475.0,228.0 12 | 38.07,198.6,192.0 13 | 28.02,198.6,192.0 14 | 43.01,427.5,228.0 15 | 42.33,190.0,228.0 16 | 47.81,304.0,228.0 17 | 52.91,380.0,228.0 18 | 39.36,139.6,192.0 19 | 56.14,342.0,228.0 20 | 40.56,380.0,228.0 21 | 42.62,475.0,228.0 22 | 41.84,427.5,228.0 23 | 28.24,139.6,192.0 24 | 8.06,139.6,192.0 25 | 44.21,139.6,192.0 26 | 52.52,380.0,228.0 27 | 53.30,380.0,228.0 28 | 41.15,380.0,228.0 29 | 52.12,342.0,228.0 30 | 37.43,427.5,228.0 31 | 38.60,475.0,228.0 32 | 55.26,304.0,228.0 33 | 52.91,266.0,228.0 34 | 41.72,198.6,192.0 35 | 42.13,475.0,228.0 36 | 53.69,190.0,228.0 37 | 38.41,237.5,228.0 38 | 30.08,237.5,228.0 39 | 37.72,332.5,228.0 40 | 42.23,475.0,228.0 41 | 36.25,237.5,228.0 42 | 50.46,342.0,228.0 43 | 43.70,427.5,228.0 44 | 39.00,237.5,228.0 45 | 53.10,380.0,228.0 46 | 41.54,427.5,228.0 47 | 35.08,427.5,228.0 48 | 15.05,349.0,192.0 49 | 40.76,380.0,228.0 50 | 26.26,237.5,228.0 51 | 32.82,380.0,228.0 52 | 39.78,332.5,228.0 53 | 46.93,190.0,228.0 54 | 33.12,237.5,228.0 55 | 49.19,304.0,228.0 56 | 14.59,139.6,192.0 57 | 14.64,198.6,192.0 58 | 41.93,475.0,228.0 59 | 9.13,198.6,192.0 60 | 50.95,304.0,228.0 61 | 33.02,332.5,228.0 62 | 54.38,304.0,228.0 63 | 51.73,266.0,228.0 64 | 9.87,310.0,192.0 65 | 50.66,190.0,228.0 66 | 48.70,266.0,228.0 67 | 55.06,342.0,228.0 68 | 44.70,139.6,192.0 69 | 30.28,332.5,228.0 70 | 40.86,190.0,228.0 71 | 71.99,485.0,146.0 72 | 34.40,374.0,170.1 73 | 28.80,313.3,175.5 74 | 33.40,425.0,153.5 75 | 36.30,425.0,151.4 76 | 29.00,375.0,126.6 77 | 37.80,475.0,181.1 78 | 40.20,469.0,137.8 79 | 33.40,425.0,153.5 80 | 28.10,388.6,157.9 81 | 41.30,531.3,141.8 82 | 33.40,425.0,153.5 83 | 25.20,318.8,155.7 84 | 41.10,401.8,147.4 85 | 35.30,362.6,164.9 86 | 28.30,323.7,183.8 87 | 28.60,379.5,153.9 88 | 35.30,362.6,164.9 89 | 24.40,286.3,144.7 90 | 35.30,362.6,164.9 91 | 39.30,439.0,186.0 92 | 40.60,389.9,145.9 93 | 35.30,362.6,164.9 94 | 24.10,337.9,174.9 95 | 46.20,374.0,170.1 96 | 42.80,313.3,175.5 97 | 49.20,425.0,153.5 98 | 46.80,425.0,151.4 99 | 45.70,375.0,126.6 100 | 55.60,475.0,181.1 101 | 54.90,469.0,137.8 102 | 49.20,425.0,153.5 103 | 34.90,388.6,157.9 104 | 46.90,531.3,141.8 105 | 49.20,425.0,153.5 106 | 33.40,318.8,155.7 107 | 54.10,401.8,147.4 108 | 55.90,362.6,164.9 109 | 49.80,323.7,183.8 110 | 47.10,379.5,153.9 111 | 55.90,362.6,164.9 112 | 38.00,286.3,144.7 113 | 55.90,362.6,164.9 114 | 56.10,439.0,186.0 115 | 59.09,389.9,145.9 116 | 22.90,362.6,164.9 117 | 35.10,337.9,174.9 118 | 61.09,374.0,170.1 119 | 59.80,313.3,175.5 120 | 60.29,425.0,153.5 121 | 61.80,425.0,151.4 122 | 56.70,375.0,126.6 123 | 68.30,475.0,181.1 124 | 66.90,469.0,137.8 125 | 60.29,425.0,153.5 126 | 50.70,388.6,157.9 127 | 56.40,531.3,141.8 128 | 60.29,425.0,153.5 129 | 55.50,318.8,155.7 130 | 68.50,401.8,147.4 131 | 71.30,362.6,164.9 132 | 74.70,323.7,183.8 133 | 52.20,379.5,153.9 134 | 71.30,362.6,164.9 135 | 67.70,286.3,144.7 136 | 71.30,362.6,164.9 137 | 66.00,439.0,186.0 138 | 74.50,389.9,145.9 139 | 71.30,362.6,164.9 140 | 49.90,337.9,174.9 141 | 63.40,374.0,170.1 142 | 64.90,313.3,175.5 143 | 64.30,425.0,153.5 144 | 64.90,425.0,151.4 145 | 60.20,375.0,126.6 146 | 72.30,475.0,181.1 147 | 69.30,469.0,137.8 148 | 64.30,425.0,153.5 149 | 55.20,388.6,157.9 150 | 58.80,531.3,141.8 151 | 64.30,425.0,153.5 152 | 66.10,318.8,155.7 153 | 73.70,401.8,147.4 154 | 77.30,362.6,164.9 155 | 80.20,323.7,183.8 156 | 54.90,379.5,153.9 157 | 77.30,362.6,164.9 158 | 72.99,286.3,144.7 159 | 77.30,362.6,164.9 160 | 71.70,439.0,186.0 161 | 79.40,389.9,145.9 162 | 77.30,362.6,164.9 163 | 59.89,337.9,174.9 164 | 64.90,374.0,170.1 165 | 66.60,313.3,175.5 166 | 65.20,425.0,153.5 167 | 66.70,425.0,151.4 168 | 62.50,375.0,126.6 169 | 74.19,475.0,181.1 170 | 70.70,469.0,137.8 171 | 65.20,425.0,153.5 172 | 57.60,388.6,157.9 173 | 59.20,531.3,141.8 174 | 65.20,425.0,153.5 175 | 68.10,318.8,155.7 176 | 75.50,401.8,147.4 177 | 79.30,362.6,164.9 178 | 56.50,379.5,153.9 179 | 79.30,362.6,164.9 180 | 76.80,286.3,144.7 181 | 79.30,362.6,164.9 182 | 73.30,439.0,186.0 183 | 82.60,389.9,145.9 184 | 79.30,362.6,164.9 185 | 67.80,337.9,174.9 186 | 11.58,222.4,189.3 187 | 24.45,222.4,189.3 188 | 24.89,222.4,189.3 189 | 29.45,222.4,189.3 190 | 40.71,222.4,189.3 191 | 10.38,233.8,197.9 192 | 22.14,233.8,197.9 193 | 22.84,233.8,197.9 194 | 27.66,233.8,197.9 195 | 34.56,233.8,197.9 196 | 12.45,194.7,165.6 197 | 24.99,194.7,165.6 198 | 25.72,194.7,165.6 199 | 33.96,194.7,165.6 200 | 37.34,194.7,165.6 201 | 15.04,190.7,162.1 202 | 21.06,190.7,162.1 203 | 26.40,190.7,162.1 204 | 35.34,190.7,162.1 205 | 40.57,190.7,162.1 206 | 12.47,212.1,180.3 207 | 20.92,212.1,180.3 208 | 24.90,212.1,180.3 209 | 34.20,212.1,180.3 210 | 39.61,212.1,180.3 211 | 10.03,230.0,195.5 212 | 20.08,230.0,195.5 213 | 24.48,230.0,195.5 214 | 31.54,230.0,195.5 215 | 35.34,230.0,195.5 216 | 9.45,190.3,161.9 217 | 22.72,190.3,161.9 218 | 28.47,190.3,161.9 219 | 38.56,190.3,161.9 220 | 40.39,190.3,161.9 221 | 10.76,166.1,176.5 222 | 25.48,166.1,176.5 223 | 21.54,166.1,176.5 224 | 28.63,166.1,176.5 225 | 33.54,166.1,176.5 226 | 7.75,168.0,121.8 227 | 17.82,168.0,121.8 228 | 24.24,168.0,121.8 229 | 32.85,168.0,121.8 230 | 39.23,168.0,121.8 231 | 18.00,213.7,181.7 232 | 30.39,213.7,181.7 233 | 45.71,213.7,181.7 234 | 50.77,213.7,181.7 235 | 53.90,213.7,181.7 236 | 13.18,213.8,181.7 237 | 17.84,213.8,181.7 238 | 40.23,213.8,181.7 239 | 47.13,213.8,181.7 240 | 49.97,213.8,181.7 241 | 13.36,229.7,195.2 242 | 22.32,229.7,195.2 243 | 24.54,229.7,195.2 244 | 31.35,229.7,195.2 245 | 40.86,229.7,195.2 246 | 19.93,238.1,186.7 247 | 25.69,238.1,186.7 248 | 30.23,238.1,186.7 249 | 39.59,238.1,186.7 250 | 44.30,238.1,186.7 251 | 13.82,250.0,187.4 252 | 24.92,250.0,187.4 253 | 29.22,250.0,187.4 254 | 38.33,250.0,187.4 255 | 42.35,250.0,187.4 256 | 13.54,212.5,159.3 257 | 26.31,212.5,159.3 258 | 31.64,212.5,159.3 259 | 42.55,212.5,159.3 260 | 42.92,212.5,159.3 261 | 13.33,212.6,159.4 262 | 25.37,212.6,159.4 263 | 37.40,212.6,159.4 264 | 44.40,212.6,159.4 265 | 47.74,212.6,159.4 266 | 19.52,212.0,159.0 267 | 31.35,212.0,159.0 268 | 38.50,212.0,159.0 269 | 45.08,212.0,159.0 270 | 47.82,212.0,159.0 271 | 15.44,231.8,174.0 272 | 26.77,231.8,174.0 273 | 33.73,231.8,174.0 274 | 42.70,231.8,174.0 275 | 45.84,231.8,174.0 276 | 17.22,251.4,188.5 277 | 29.93,251.4,188.5 278 | 29.65,251.4,188.5 279 | 36.97,251.4,188.5 280 | 43.58,251.4,188.5 281 | 13.12,251.4,188.5 282 | 24.43,251.4,188.5 283 | 32.66,251.4,188.5 284 | 36.64,251.4,188.5 285 | 44.21,251.4,188.5 286 | 13.62,181.4,169.6 287 | 21.60,181.4,169.6 288 | 27.77,181.4,169.6 289 | 35.57,181.4,169.6 290 | 45.37,181.4,169.6 291 | 7.32,182.0,170.2 292 | 21.50,182.0,170.2 293 | 31.27,182.0,170.2 294 | 43.50,182.0,170.2 295 | 48.67,182.0,170.2 296 | 7.40,168.9,158.3 297 | 23.51,168.9,158.3 298 | 31.12,168.9,158.3 299 | 39.15,168.9,158.3 300 | 48.15,168.9,158.3 301 | 22.50,290.4,168.1 302 | 34.67,290.4,168.1 303 | 34.74,290.4,168.1 304 | 45.08,290.4,168.1 305 | 48.97,290.4,168.1 306 | 23.14,277.1,160.6 307 | 41.89,277.1,160.6 308 | 48.28,277.1,160.6 309 | 51.04,277.1,160.6 310 | 55.64,277.1,160.6 311 | 22.95,295.7,171.5 312 | 35.23,295.7,171.5 313 | 39.94,295.7,171.5 314 | 48.72,295.7,171.5 315 | 52.04,295.7,171.5 316 | 21.02,251.8,146.1 317 | 33.36,251.8,146.1 318 | 33.94,251.8,146.1 319 | 44.14,251.8,146.1 320 | 45.37,251.8,146.1 321 | 15.36,249.1,158.1 322 | 28.68,249.1,158.1 323 | 30.85,249.1,158.1 324 | 42.03,249.1,158.1 325 | 51.06,249.1,158.1 326 | 21.78,252.3,146.3 327 | 42.29,252.3,146.3 328 | 50.60,252.3,146.3 329 | 55.83,252.3,146.3 330 | 60.95,252.3,146.3 331 | 23.52,246.8,143.3 332 | 42.22,246.8,143.3 333 | 52.50,246.8,143.3 334 | 60.32,246.8,143.3 335 | 66.42,246.8,143.3 336 | 23.80,275.1,159.5 337 | 38.77,275.1,159.5 338 | 51.33,275.1,159.5 339 | 56.85,275.1,159.5 340 | 58.61,275.1,159.5 341 | 21.91,297.2,174.8 342 | 36.99,297.2,174.8 343 | 47.40,297.2,174.8 344 | 51.96,297.2,174.8 345 | 56.74,297.2,174.8 346 | 17.57,213.7,154.8 347 | 33.73,213.7,154.8 348 | 40.15,213.7,154.8 349 | 46.64,213.7,154.8 350 | 50.08,213.7,154.8 351 | 17.37,213.5,154.6 352 | 33.70,213.5,154.6 353 | 45.94,213.5,154.6 354 | 51.43,213.5,154.6 355 | 59.30,213.5,154.6 356 | 30.45,277.2,160.7 357 | 47.71,277.2,160.7 358 | 63.14,277.2,160.7 359 | 66.82,277.2,160.7 360 | 66.95,277.2,160.7 361 | 27.42,218.2,140.8 362 | 35.96,218.2,140.8 363 | 55.51,218.2,140.8 364 | 61.99,218.2,140.8 365 | 63.53,218.2,140.8 366 | 18.02,214.9,155.6 367 | 38.60,214.9,155.6 368 | 52.20,214.9,155.6 369 | 53.96,214.9,155.6 370 | 56.63,214.9,155.6 371 | 15.34,218.9,158.5 372 | 26.05,218.9,158.5 373 | 30.22,218.9,158.5 374 | 37.27,218.9,158.5 375 | 46.23,218.9,158.5 376 | 16.28,376.0,214.6 377 | 25.62,376.0,214.6 378 | 31.97,376.0,214.6 379 | 36.30,376.0,214.6 380 | 43.06,376.0,214.6 381 | 67.57,500.0,140.0 382 | 57.23,475.0,142.0 383 | 81.75,315.0,145.0 384 | 64.02,505.0,195.0 385 | 78.80,451.0,165.0 386 | 41.37,516.0,162.0 387 | 60.28,520.0,170.0 388 | 56.83,528.0,185.0 389 | 51.02,520.0,175.0 390 | 55.55,385.0,158.0 391 | 44.13,500.1,200.0 392 | 39.38,450.1,200.0 393 | 55.65,397.0,167.0 394 | 47.28,333.0,167.0 395 | 44.33,334.0,189.0 396 | 52.30,405.0,175.0 397 | 49.25,200.0,190.0 398 | 41.37,516.0,162.0 399 | 29.16,145.0,184.0 400 | 39.40,160.0,182.0 401 | 39.30,234.0,189.0 402 | 67.87,250.0,159.0 403 | 58.52,475.0,162.0 404 | 53.58,285.0,163.0 405 | 59.00,356.0,160.0 406 | 76.24,275.0,162.0 407 | 69.84,500.0,151.0 408 | 14.40,165.0,163.8 409 | 19.42,165.0,175.1 410 | 20.73,178.0,179.9 411 | 14.94,167.4,175.5 412 | 21.29,172.4,156.8 413 | 23.08,173.5,164.8 414 | 15.52,167.0,164.0 415 | 15.82,173.8,172.3 416 | 12.55,190.3,166.6 417 | 8.49,250.0,191.8 418 | 15.61,213.5,159.2 419 | 12.18,194.7,170.2 420 | 11.98,251.4,192.9 421 | 16.88,165.0,163.8 422 | 33.09,165.0,175.1 423 | 34.24,178.0,179.9 424 | 31.81,167.4,175.5 425 | 29.75,172.4,156.8 426 | 33.01,173.5,164.8 427 | 32.90,167.0,164.0 428 | 29.55,173.8,172.3 429 | 19.42,190.3,166.6 430 | 24.66,250.0,191.8 431 | 29.59,213.5,159.2 432 | 24.28,194.7,170.2 433 | 20.73,251.4,192.9 434 | 26.20,165.0,163.8 435 | 46.39,165.0,175.1 436 | 39.16,178.0,179.9 437 | 41.20,167.4,175.5 438 | 33.69,172.4,156.8 439 | 38.20,173.5,164.8 440 | 41.41,167.0,164.0 441 | 37.81,173.8,172.3 442 | 24.85,190.3,166.6 443 | 27.22,250.0,191.8 444 | 44.64,213.5,159.2 445 | 37.27,194.7,170.2 446 | 33.27,251.4,192.9 447 | 36.56,165.0,163.8 448 | 53.72,165.0,175.1 449 | 48.59,178.0,179.9 450 | 51.72,167.4,175.5 451 | 35.85,172.4,156.8 452 | 53.77,173.5,164.8 453 | 53.46,167.0,164.0 454 | 48.99,173.8,172.3 455 | 31.72,190.3,166.6 456 | 39.64,250.0,191.8 457 | 51.26,213.5,159.2 458 | 43.39,194.7,170.2 459 | 39.27,251.4,192.9 460 | 37.96,165.0,163.8 461 | 55.02,165.0,175.1 462 | 49.99,178.0,179.9 463 | 53.66,167.4,175.5 464 | 37.68,172.4,156.8 465 | 56.06,173.5,164.8 466 | 56.81,167.0,164.0 467 | 50.94,173.8,172.3 468 | 33.56,190.3,166.6 469 | 41.16,250.0,191.8 470 | 52.96,213.5,159.2 471 | 44.28,194.7,170.2 472 | 40.15,251.4,192.9 473 | 57.03,446.0,162.0 474 | 44.42,446.0,162.0 475 | 51.02,446.0,162.0 476 | 53.39,446.0,162.0 477 | 35.36,446.0,162.0 478 | 25.02,446.0,162.0 479 | 23.35,446.0,162.0 480 | 52.01,446.0,162.0 481 | 38.02,446.0,162.0 482 | 39.30,446.0,162.0 483 | 61.07,446.0,162.0 484 | 56.14,446.0,162.0 485 | 55.25,446.0,162.0 486 | 54.77,446.0,162.0 487 | 50.24,387.0,157.0 488 | 46.68,387.0,157.0 489 | 46.68,387.0,157.0 490 | 22.75,387.0,157.0 491 | 25.51,387.0,157.0 492 | 34.77,387.0,157.0 493 | 36.84,387.0,157.0 494 | 45.90,387.0,157.0 495 | 41.67,387.0,157.0 496 | 56.34,387.0,157.0 497 | 47.97,387.0,157.0 498 | 61.46,387.0,157.0 499 | 44.03,355.0,145.0 500 | 55.45,355.0,145.0 501 | 55.55,491.0,210.0 502 | 57.92,491.0,201.0 503 | 25.61,491.0,210.0 504 | 33.49,491.0,210.0 505 | 59.59,491.0,210.0 506 | 29.55,491.0,201.0 507 | 37.92,491.0,201.0 508 | 61.86,491.0,201.0 509 | 62.05,424.0,178.0 510 | 32.01,424.0,178.0 511 | 72.10,424.0,168.0 512 | 39.00,424.0,178.0 513 | 65.70,424.0,178.0 514 | 32.11,424.0,168.0 515 | 40.29,424.0,168.0 516 | 74.36,424.0,168.0 517 | 21.97,202.0,206.0 518 | 9.85,202.0,206.0 519 | 15.07,202.0,206.0 520 | 23.25,202.0,206.0 521 | 43.73,284.0,179.0 522 | 13.40,284.0,179.0 523 | 24.13,284.0,179.0 524 | 44.52,284.0,179.0 525 | 62.94,359.0,154.0 526 | 59.49,359.0,154.0 527 | 25.12,359.0,154.0 528 | 23.64,359.0,154.0 529 | 35.75,359.0,154.0 530 | 38.61,359.0,154.0 531 | 68.75,359.0,154.0 532 | 66.78,359.0,154.0 533 | 23.85,436.0,218.0 534 | 32.07,289.0,192.0 535 | 11.65,289.0,192.0 536 | 19.20,393.0,192.0 537 | 48.85,393.0,192.0 538 | 39.60,393.0,192.0 539 | 43.94,480.0,192.0 540 | 34.57,480.0,192.0 541 | 54.32,480.0,192.0 542 | 24.40,480.0,192.0 543 | 15.62,333.0,192.0 544 | 21.86,255.0,192.0 545 | 10.22,255.0,192.0 546 | 14.60,289.0,192.0 547 | 18.75,255.0,192.0 548 | 31.97,333.0,192.0 549 | 23.40,333.0,192.0 550 | 25.57,289.0,192.0 551 | 41.68,333.0,192.0 552 | 27.74,393.0,192.0 553 | 8.20,255.0,192.0 554 | 9.62,158.8,185.7 555 | 25.42,239.6,185.7 556 | 15.69,238.2,185.7 557 | 27.94,181.9,185.7 558 | 32.63,193.5,185.7 559 | 17.24,255.5,185.7 560 | 19.77,272.8,185.7 561 | 39.44,239.6,185.7 562 | 25.75,220.8,185.7 563 | 33.08,397.0,185.7 564 | 24.07,382.5,185.7 565 | 21.82,210.7,185.7 566 | 21.07,158.8,185.7 567 | 14.84,295.8,185.7 568 | 32.05,255.5,185.7 569 | 11.96,203.5,185.7 570 | 25.45,397.0,185.7 571 | 22.49,381.4,185.7 572 | 25.22,295.8,185.7 573 | 39.70,228.0,185.7 574 | 13.09,220.8,185.7 575 | 38.70,316.1,185.7 576 | 7.51,135.7,185.7 577 | 17.58,238.1,185.7 578 | 21.18,339.2,185.7 579 | 18.20,135.7,185.7 580 | 17.20,193.5,185.7 581 | 22.63,203.5,185.7 582 | 21.86,290.2,185.7 583 | 12.37,181.9,185.7 584 | 25.73,170.3,185.7 585 | 37.81,210.7,185.7 586 | 21.92,228.0,185.7 587 | 33.04,290.2,185.7 588 | 14.54,381.4,185.7 589 | 26.91,238.2,185.7 590 | 8.00,186.2,185.7 591 | 31.90,339.2,185.7 592 | 10.34,238.1,185.7 593 | 19.77,252.5,185.7 594 | 37.44,382.5,185.7 595 | 11.48,252.5,185.7 596 | 24.44,316.1,185.7 597 | 17.60,186.2,185.7 598 | 10.73,170.3,185.7 599 | 31.38,272.8,185.7 600 | 13.22,339.0,197.0 601 | 20.97,339.0,197.0 602 | 27.04,339.0,197.0 603 | 32.04,339.0,197.0 604 | 35.17,339.0,197.0 605 | 36.45,339.0,197.0 606 | 38.89,339.0,197.0 607 | 6.47,236.0,194.0 608 | 12.84,236.0,194.0 609 | 18.42,236.0,194.0 610 | 21.95,236.0,194.0 611 | 24.10,236.0,193.0 612 | 25.08,236.0,193.0 613 | 21.26,277.0,191.0 614 | 25.97,277.0,191.0 615 | 11.36,277.0,191.0 616 | 31.25,277.0,191.0 617 | 32.33,277.0,191.0 618 | 33.70,277.0,191.0 619 | 9.31,254.0,198.0 620 | 26.94,254.0,198.0 621 | 27.63,254.0,198.0 622 | 29.79,254.0,198.0 623 | 34.49,307.0,193.0 624 | 36.15,307.0,193.0 625 | 12.54,307.0,193.0 626 | 27.53,307.0,193.0 627 | 32.92,307.0,193.0 628 | 9.99,236.0,193.0 629 | 7.84,200.0,180.0 630 | 12.25,200.0,180.0 631 | 11.17,225.0,181.0 632 | 17.34,225.0,181.0 633 | 17.54,325.0,184.0 634 | 30.57,325.0,184.0 635 | 14.20,275.0,183.0 636 | 24.50,275.0,183.0 637 | 15.58,300.0,184.0 638 | 26.85,300.0,184.0 639 | 26.06,375.0,186.0 640 | 38.21,375.0,186.0 641 | 43.70,400.0,187.0 642 | 30.14,400.0,187.0 643 | 12.73,250.0,182.0 644 | 20.87,250.0,182.0 645 | 20.28,350.0,186.0 646 | 34.29,350.0,186.0 647 | 19.54,203.5,203.5 648 | 47.71,250.2,203.5 649 | 43.38,157.0,192.0 650 | 29.89,141.3,203.5 651 | 6.90,166.8,203.5 652 | 33.19,122.6,203.5 653 | 4.90,183.9,203.5 654 | 4.57,102.0,192.0 655 | 25.46,102.0,192.0 656 | 24.29,122.6,203.5 657 | 33.95,166.8,203.5 658 | 11.41,200.0,192.0 659 | 20.59,108.3,203.5 660 | 25.89,305.3,203.5 661 | 29.23,108.3,203.5 662 | 31.02,116.0,192.0 663 | 10.39,141.3,203.5 664 | 33.66,157.0,192.0 665 | 27.87,133.0,192.0 666 | 19.35,250.2,203.5 667 | 11.39,173.0,192.0 668 | 12.79,192.0,192.0 669 | 39.32,192.0,192.0 670 | 4.78,153.0,192.0 671 | 16.11,288.0,192.0 672 | 43.38,305.3,203.5 673 | 20.42,236.0,192.0 674 | 6.94,173.0,192.0 675 | 15.03,212.0,203.5 676 | 13.57,236.0,192.0 677 | 32.53,183.9,203.5 678 | 15.75,166.8,203.5 679 | 7.68,102.0,192.0 680 | 38.80,288.0,192.0 681 | 33.00,212.0,203.5 682 | 17.28,102.0,192.0 683 | 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