├── .gitignore ├── LICENSE ├── _config.yml ├── _layouts └── default.html ├── assets └── css │ └── style.scss ├── img ├── art.png ├── docs.png └── github.png └── index.md /.gitignore: -------------------------------------------------------------------------------- 1 | minimal/* -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. 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It presently includes: 50 | > 51 | > * RejectionABC 52 | > * PMCABC (Population Monte Carlo ABC) 53 | > * SMCABC (Sequential Monte Carlo ABC) 54 | > * RSMCABC (Replenishment SMC-ABC) 55 | > * APMCABC (Adaptive Population Monte Carlo ABC) 56 | > * SABC (Simulated Annealing ABC) 57 | > * ABCsubsim (ABC using subset simulation) 58 | > * PMC (Population Monte Carlo) using approximations of likelihood functions 59 | > * Random Forest Model Selection Scheme 60 | > * Semi-automatic summary selection (with Neural networks) 61 | > * summary selection using distance learning (with Neural networks) 62 | > 63 | > ABCpy addresses the needs of domain scientists and data scientists by providing 64 | > 65 | > * a fully modularized framework that is easy to use and easy to extend, 66 | > * a quick way to integrate your generative model into the framework (from C++, R etc.) and 67 | > * a non-intrusive, user-friendly way to parallelize inference computations (for your laptop to clusters, supercomputers and AWS) 68 | > * an intuitive way to perform inference on hierarchical models or more generally on Bayesian networks 69 | 70 | [Repo]( 71 | https://github.com/eth-cscs/abcpy) | 72 | [Docs]( 73 | http://abcpy.readthedocs.io/en/) | 74 | [Article]( 75 | https://arxiv.org/abs/1711.04694) 76 | 77 | --- 78 | 79 | 80 | ## ABrox 81 | 82 | > ABrox is a python package for Approximate Bayesian Computation accompanied by 83 | a user-friendly graphical interface. 84 | 85 | [Repo]( 86 | https://github.com/stroblmar/ABrox) | 87 | [Article]( 88 | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193981) 89 | 90 | --- 91 | 92 | 93 | ## ABC-SysBio 94 | 95 | > ABC-SysBio implements likelihood free parameter inference and model selection 96 | in dynamical systems. It is designed to work with both stochastic and 97 | deterministic models written in Systems Biology Markup Language (SBML). 98 | ABC-SysBio is a Python package that combines three algorithms: ABC rejection 99 | sampler, ABC SMC for parameter inference and ABC SMC for model selection. 100 | 101 | [Docs]( 102 | http://www.theosysbio.bio.ic.ac.uk/resources/abc-sysbio/) | 103 | [Article]( 104 | https://academic.oup.com/bioinformatics/article/26/14/1797/178572) 105 | 106 | --- 107 | 108 | 109 | ## astroABC 110 | 111 | > astroABC is a Python implementation of an Approximate Bayesian Computation 112 | Sequential Monte Carlo (ABC SMC) sampler for parameter estimation. 113 | > 114 | > Key features 115 | > * Parallel sampling using MPI or multiprocessing 116 | > * MPI communicator can be split so both the sampler, and simulation launched 117 | by each particle, can run in parallel 118 | > * A Sequential Monte Carlo sampler (see e.g. Toni et al. 2009, Beaumont et 119 | al. 2009, Sisson & Fan 2010) 120 | > * A method for iterative adapting tolerance levels using the qth quantile of 121 | the distance for t iterations (Turner & Van Zandt (2012)) 122 | > * Scikit-learn covariance matrix estimation using Ledoit-Wolf shrinkage for 123 | singular matrices 124 | > * A module for specifying particle covariance using method proposed by Turner 125 | & Van Zandt (2012), optimal covariance matrix for a multivariate normal 126 | perturbation kernel, local covariance estimate using scikit-learn KDTree method 127 | for nearest neighbours (Filippi et al 2013) and a weighted covariance 128 | (Beaumont et al 2009) 129 | > * Restart files output frequently so an interrupted run can be resumed at any 130 | iteration 131 | > * Output and restart files are backed up every iteration 132 | > * User defined distance metric and simulation methods 133 | > * A class for specifying heterogeneous parameter priors 134 | > * Methods for drawing from any non-standard prior PDF e.g using Planck/WMAP 135 | chains 136 | > * A module for specifying a constant, linear, log or exponential tolerance 137 | level 138 | > * Well-documented examples and sample scripts 139 | 140 | [Repo]( 141 | https://github.com/EliseJ/astroABC) | 142 | [Docs]( 143 | https://github.com/EliseJ/astroABC/wiki) | 144 | [Article]( 145 | https://arxiv.org/abs/1608.07606) 146 | 147 | --- 148 | 149 | 150 | ## A-NICE-MC 151 | 152 | > A-NICE-MC is a framework that trains a parametric Markov Chain Monte Carlo proposal. It achieves higher performance than traditional nonparametric proposals, such as Hamiltonian Monte Carlo (HMC). 153 | > 154 | > A-NICE-MC stands for Adversarial Non-linear Independent Component Estimation Monte Carlo, in that: 155 | > 156 | > * The framework utilizes a parametric proposal for Markov Chain Monte Carlo (MC). 157 | > * The proposal is represented through Non-linear Independent Component Estimation (NICE). 158 | > * The NICE network is trained through adversarial methods (A); see [jiamings/markov-chain-gan](https://github.com/jiamings/markov-chain-gan). 159 | 160 | [Repo]( 161 | https://github.com/ermongroup/a-nice-mc) | 162 | [Article]( 163 | https://arxiv.org/abs/1706.07561) 164 | 165 | --- 166 | 167 | 168 | ## bmcmc 169 | 170 | > bmcmc is a general purpose mcmc package which should be useful for Bayesian 171 | data analysis. It uses an adaptive scheme for automatic tuning of proposal 172 | distributions. It can also handle hierarchical Bayesian models via 173 | Metropolis-Within-Gibbs scheme. 174 | 175 | [Repo]( 176 | https://github.com/sanjibs/bmcmc/) | 177 | [Docs]( 178 | https://bmcmc.readthedocs.io) | 179 | [Article]( 180 | https://arxiv.org/abs/1706.01629) 181 | 182 | --- 183 | 184 | 185 | ## CheKiPEUQ 186 | 187 | > CheKiPEUQ is a pythonMCMC code for Parameter estimation for complex physical problems. The CheKiPEUQ software provides tools for finding physically realistic parameter estimates, graphs of the parameter estimate positions within parameter space, and plots of the final simulation results. 188 | 189 | [Repo]( 190 | https://github.com/AdityaSavara/CheKiPEUQ) | 191 | [Article]( 192 | https://doi.org/10.1002/cctc.202000953) 193 | 194 | --- 195 | 196 | 197 | ## CosmoABC 198 | 199 | > Package which enables parameter inference using an Approximate 200 | Bayesian Computation (ABC) algorithm. The code was originally designed for 201 | cosmological parameter inference from galaxy clusters number counts based on 202 | Sunyaev-Zel’dovich measurements. In this context, the cosmological simulations 203 | were performed using the NumCosmo library. 204 | 205 | [Repo]( 206 | https://github.com/COINtoolbox/CosmoABC) | 207 | [Docs]( 208 | https://cosmoabc.readthedocs.io) | 209 | [Article]( 210 | https://arxiv.org/abs/1504.06129) 211 | 212 | --- 213 | 214 | 215 | ## CPNest 216 | 217 | > Parallel nested sampling in python. 218 | > CPNest is a python package for performing Bayesian inference using the nested sampling algorithm. It is designed to be simple for the user to provide a model via a set of parameters, their bounds and a log-likelihood function. An optional log-prior function can be given for non-uniform prior distributions. 219 | 220 | [Repo]( 221 | https://github.com/johnveitch/cpnest) | 222 | [Docs]( 223 | https://johnveitch.github.io/cpnest/) 224 | 225 | --- 226 | 227 | 228 | ## dynesty 229 | 230 | > A Dynamic Nested Sampling package for computing Bayesian posteriors and 231 | evidences. 232 | 233 | [Repo]( 234 | https://github.com/joshspeagle/dynesty) | 235 | [Docs]( 236 | https://dynesty.readthedocs.io/) | 237 | [Article]( 238 | https://arxiv.org/abs/1904.02180) 239 | 240 | --- 241 | 242 | 243 | ## dyPolyChord 244 | 245 | > dyPolyChord implements dynamic nested sampling using the efficient PolyChord sampler to provide state-of-the-art nested sampling performance. Any likelihoods and priors which work with PolyChord can be used (Python, C++ or Fortran), and the output files produced are in the PolyChord format. 246 | 247 | [Repo]( 248 | https://github.com/ejhigson/dyPolyChord) | 249 | [Docs]( 250 | https://dypolychord.readthedocs.io/en/) 251 | 252 | --- 253 | 254 | 255 | ## Edward2 256 | 257 | > Edward2 is a probabilistic programming language in TensorFlow and Python. It 258 | extends the TensorFlow ecosystem so that one can declare models as probabilistic 259 | programs and manipulate a model's computation for flexible training, latent 260 | variable inference, and predictions. 261 | 262 | * Original project: [Edward](http://edwardlib.org/) ([Tran et al. 263 | (2016)](https://arxiv.org/abs/1610.09787)) 264 | 265 | [Repo]( 266 | https://github.com/google/edward2) 267 | 268 | --- 269 | 270 | 271 | ## ELFI 272 | 273 | > ELFI is a statistical software package written in Python for likelihood-free 274 | inference (LFI) such as Approximate Bayesian Computation (ABC). The term LFI 275 | refers to a family of inference methods that replace the use of the likelihood 276 | function with a data generating simulator function. ELFI features an easy to use 277 | generative modeling syntax and supports parallelized inference out of the box. 278 | 279 | [Repo]( 280 | https://github.com/elfi-dev/elfi) | 281 | [Docs]( 282 | http://elfi.readthedocs.io/) | 283 | [Article]( 284 | http://www.jmlr.org/papers/v19/17-374.html) 285 | 286 | --- 287 | 288 | 289 | ## emcee 290 | 291 | > emcee is an MIT licensed pure-Python implementation of Goodman & Weare’s 292 | [Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble 293 | sampler](http://msp.berkeley.edu/camcos/2010/5-1/p04.xhtml). It's designed for 294 | Bayesian parameter estimation and it's really sweet! 295 | 296 | [Repo]( 297 | https://github.com/dfm/emcee) | 298 | [Docs]( 299 | https://emcee.readthedocs.io) | 300 | [Article]( 301 | https://arxiv.org/abs/1202.3665) 302 | 303 | --- 304 | 305 | 306 | ## hmc 307 | 308 | > A simple Hamiltonian MCMC sampler. 309 | 310 | [Repo]( 311 | https://github.com/bd-j/hmc) 312 | 313 | --- 314 | 315 | 316 | ## hoppMCMC 317 | 318 | > An adaptive basin-hopping Markov-chain Monte Carlo algorithm for Bayesian 319 | optimisation. Python implementation of the hoppMCMC algorithm aiming to identify 320 | and sample from the high-probability regions of a posterior distribution. The 321 | algorithm combines three strategies: (i) parallel MCMC, (ii) adaptive Gibbs 322 | sampling and (iii) simulated annealing. Overall, hoppMCMC resembles the 323 | basin-hopping algorithm implemented in the optimize module of scipy, but it is 324 | developed for a wide range of modelling approaches including stochastic models 325 | with or without time-delay. 326 | 327 | [Repo]( 328 | https://github.com/kerguler/hoppMCMC) 329 | 330 | --- 331 | 332 | 333 | ## kombine 334 | 335 | > kombine is an ensemble sampler built for efficiently exploring multimodal 336 | distributions. By using estimates of ensemble’s instantaneous distribution as a 337 | proposal, it achieves very fast burnin, followed by sampling with very short 338 | autocorrelation times. 339 | 340 | [Repo]( 341 | https://github.com/bfarr/kombine) | 342 | [Docs]( 343 | https://pages.uoregon.edu/bfarr/kombine/index.html) 344 | 345 | --- 346 | 347 | 348 | ## MC3 349 | 350 | > Multi-Core Markov-Chain Monte Carlo (MC3) is a powerful Bayesian-statistics 351 | tool that offers: 352 | > 353 | > * Levenberg-Marquardt least-squares optimization. 354 | > * Markov-chain Monte Carlo (MCMC) posterior-distribution sampling following 355 | the: 356 | > * Metropolis-Hastings algorithm with Gaussian proposal distribution, 357 | > * Differential-Evolution MCMC (DEMC), or 358 | > * DEMCzs (Snooker). 359 | 360 | [Repo]( 361 | https://github.com/pcubillos/MCcubed) | 362 | [Docs]( 363 | http://pcubillos.github.io/MCcubed/) | 364 | [Article]( 365 | http://adsabs.harvard.edu/abs/2017AJ....153....3C) 366 | 367 | --- 368 | 369 | 370 | ## Nested Sampling 371 | 372 | > Flexible and efficient Python implementation of the nested sampling algorithm. 373 | This implementation is geared towards allowing statistical physicists to use 374 | this method for thermodynamic analysis but is also being used by 375 | astrophysicists. 376 | > 377 | > This implementation uses the language of statistical mechanics (partition 378 | function, phase space, configurations, energy, density of states) rather than 379 | the language of Bayesian sampling (likelihood, prior, evidence). This is simply 380 | for convenience, the method is the same. 381 | > 382 | > The package goes beyond the bare implementation of the method providing: 383 | > 384 | >* built-in parallelisation on single computing node (max total number of cpu 385 | threads on a single machine) 386 | >* built-in Pyro4-based parallelisation by distributed computing, ideal to run 387 | calculations on a cluster or across a network 388 | >* ability to save and restart from checkpoint binary files, ideal for very 389 | long calculations 390 | >* scripts to compute heat capacities and perform error analysis 391 | integration with the MCpele package to implement efficient Monte Carlo walkers. 392 | 393 | * [Official site](http://www.inference.phy.cam.ac.uk/bayesys/) 394 | * [Compared to annealing](http://www.mrao.cam.ac.uk/~steve/malta2009/images/nestposter.pdf) 395 | 396 | [Repo]( 397 | https://github.com/js850/nested_sampling) | 398 | [Docs]( 399 | http://js850.github.io/nested_sampling/) | 400 | [Article]( 401 | http://projecteuclid.org/euclid.ba/1340370944) 402 | 403 | --- 404 | 405 | 406 | ## Nestle 407 | 408 | > Pure Python, MIT-licensed implementation of nested sampling algorithms. 409 | > Nested Sampling is a computational approach for integrating posterior 410 | probability in order to compare models in Bayesian statistics. It is similar to 411 | Markov Chain Monte Carlo (MCMC) in that it generates samples that can be used to 412 | estimate the posterior probability distribution. Unlike MCMC, the nature of the 413 | sampling also allows one to calculate the integral of the distribution. It also 414 | happens to be a pretty good method for robustly finding global maxima. 415 | 416 | [Repo]( 417 | https://github.com/kbarbary/nestle) | 418 | [Docs]( 419 | http://kylebarbary.com/nestle/) 420 | 421 | --- 422 | 423 | 424 | ## NUTS 425 | 426 | > No-U-Turn Sampler (NUTS) for python 427 | > This package implements the No-U-Turn Sampler (NUTS) algorithm 6 from the NUTS 428 | paper (Hoffman & Gelman, 2011). 429 | 430 | [Repo]( 431 | https://github.com/mfouesneau/NUTS) | 432 | [Docs]( 433 | http://js850.github.io/nested_sampling/) | 434 | [Article]( 435 | https://arxiv.org/abs/1111.4246) 436 | 437 | --- 438 | 439 | 440 | ## pgmpy 441 | 442 | > Python library for working with Probabilistic Graphical Models. 443 | 444 | [Repo]( 445 | https://github.com/pgmpy/pgmpy) | 446 | [Docs]( 447 | http://pgmpy.org/) 448 | 449 | --- 450 | 451 | 452 | ## ptemcee 453 | 454 | > Fork of Daniel Foreman-Mackey's emcee to implement parallel 455 | tempering more robustly. As far as possible, it is designed as a drop-in 456 | replacement for emcee. If you're trying to characterise awkward, multi-modal 457 | probability distributions, then ptemcee is your friend. 458 | 459 | [Repo]( 460 | https://github.com/willvousden/ptemcee) | 461 | [Docs]( 462 | http://ptemcee.readthedocs.io) | 463 | [Article]( 464 | https://arxiv.org/abs/1501.05823) 465 | 466 | --- 467 | 468 | 469 | ## PTMCMCSampler 470 | 471 | > MPI enabled Parallel Tempering MCMC code written in Python. 472 | 473 | [Repo]( 474 | https://github.com/jellis18/PTMCMCSampler) | 475 | [Docs]( 476 | http://jellis18.github.io/PTMCMCSampler/) 477 | 478 | --- 479 | 480 | 481 | ## ptmpi 482 | 483 | > Python class that coordinates an MPI implementation of parallel tempering. 484 | > Supports a fully parallelised implementation of parallel tempering using 485 | mpi4py (message passing interface for python). Each replica runs as a separate 486 | parallel process and they communicate via an mpi4py object. To minimise message 487 | passing the replicas stay in place and only the temperatures are exchanged 488 | between the processes. It is this exchange of temperatures that ptmpi handles. 489 | 490 | * [Blog entry](https://chrisdoesscience.wordpress.com/2016/07/17/parallelised-parallel-tempering-with-mpi/) 491 | 492 | [Repo]( 493 | https://github.com/chris-n-self/ptmpi) | 494 | [Docs]( 495 | http://jellis18.github.io/PTMCMCSampler/) 496 | 497 | --- 498 | 499 | 500 | ## pyABC 501 | 502 | > pyABC is a framework for distributed, likelihood-free inference. That means, 503 | if you have a model and some data and want to know the posterior distribution 504 | over the model parameters, i.e. you want to know with which probability which 505 | parameters explain the observed data, then pyABC might be for you. 506 | > 507 | > All you need is some way to numerically draw samples from the model, given the 508 | model parameters. pyABC “inverts” the model for you and tells you which 509 | parameters were well matching and which ones not. You do not need to 510 | analytically calculate the likelihood function. 511 | > 512 | > pyABC runs efficiently on multi-core machines and distributed cluster setups. 513 | It is easy to use and flexibly extensible. 514 | 515 | [Repo]( 516 | https://github.com/icb-dcm/pyabc) | 517 | [Docs]( 518 | https://pyabc.readthedocs.io/en/latest/index.html) | 519 | [Article]( 520 | https://www.biorxiv.org/content/early/2017/07/17/162552) 521 | 522 | --- 523 | 524 | 525 | ## PyDREAM 526 | 527 | > A Python implementation of the MT-DREAM(ZS) algorithm. 528 | 529 | [Repo]( 530 | https://github.com/LoLab-VU/PyDREAM) | 531 | [Docs]( 532 | https://pydream.readthedocs.io/en/latest/) | 533 | [Article]( 534 | https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011WR010608) 535 | 536 | --- 537 | 538 | 539 | ## pyhmc 540 | 541 | > This package is a straight-forward port of the functions `hmc2.m` and 542 | `hmc2_opt.m` from the MCMCstuff matlab toolbox written by Aki Vehtari. The code 543 | is originally based on the functions hmc.m from the netlab toolbox written by 544 | Ian T Nabney. The portion of algorithm involving "windows" is derived from the C code for this function included in the Software for Flexible Bayesian Modeling written by Radford Neal. 545 | 546 | [Repo]( 547 | https://github.com/rmcgibbo/pyhmc) | 548 | [Docs]( 549 | https://pythonhosted.org/pyhmc/) | 550 | [Article]( 551 | https://arxiv.org/abs/1206.1901v1) 552 | 553 | --- 554 | 555 | 556 | ## PyJAGS 557 | 558 | > PyJAGS provides a Python interface to JAGS, a program for analysis of 559 | Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation. 560 | 561 | * [Blog article](https://martynplummer.wordpress.com/2016/01/11/pyjags/) 562 | 563 | [Repo]( 564 | https://github.com/tmiasko/pyjags) | 565 | [Docs]( 566 | https://pyjags.readthedocs.io) 567 | 568 | --- 569 | 570 | 571 | ## PyMC3 572 | 573 | > PyMC3 is a probabilistic programming module for Python that allows users 574 | to fit Bayesian models using a variety of numerical methods, most notably 575 | Markov chain Monte Carlo (MCMC) and variational inference (VI). Its flexibility 576 | and extensibility make it applicable to a large suite of problems. Along with 577 | core model specification and fitting functionality, PyMC3 includes 578 | functionality for summarizing output and for model diagnostics. 579 | 580 | * Tutorials: 581 | 1. [Bayesian Modelling in Python](https://github.com/markdregan/ 582 | Bayesian-Modelling-in-Python) 583 | 1. [Using PyMC3](http://people.duke.edu/~ccc14/sta-663-2017/19A_PyMC3.html) 584 | 1. [Tutorial 5a: Parameter estimation with Markov chain Monte Carlo](http:// 585 | bebi103.caltech.edu.s3-website-us-east-1.amazonaws.com/2017/tutorials/ 586 | t5a_mcmc.html) 587 | * Books: 588 | 1. [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/ 589 | Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) 590 | 1. [Statistical Rethinking with Python and PyMC3](https://github.com/ 591 | aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3) 592 | 593 | [Repo]( 594 | https://github.com/pymc-devs/pymc3) | 595 | [Docs]( 596 | http://docs.pymc.io/intro.html) | 597 | [Article]( 598 | https://arxiv.org/abs/1507.08050) 599 | 600 | --- 601 | 602 | 603 | ## PyMCMC 604 | 605 | > Simple implementation of the Metropolis-Hastings algorithm for Markov Chain 606 | Monte Carlo sampling of multidimensional spaces. 607 | > The implementation is minimalistic. All that is required is a funtion which 608 | accepts an iterable of parameter values, and returns the positive log likelihood 609 | at that point. 610 | 611 | [Repo]( 612 | https://github.com/gmcgoldr/pymcmc) 613 | 614 | --- 615 | 616 | 617 | ## py-mcmc 618 | 619 | > A python module implementing some generic MCMC routines. The main purpose of this module is to serve as a simple MCMC framework for generic models. Probably the most useful contribution at the moment, is that it can be used to train Gaussian process (GP) models implemented in the [GPy package](http://sheffieldml.github.io/GPy/). 620 | > 621 | > The code features the following things at the moment: 622 | > 623 | > * Fully object oriented. The models can be of any type as soon as they offer the right interface. 624 | > * Random walk proposals. 625 | > * Metropolis Adjusted Langevin Dynamics. 626 | > * The MCMC chains are stored in fast HDF5 format using PyTables. 627 | > * A mean function can be added to the (GP) models of the GPy package. 628 | 629 | [Repo]( 630 | https://github.com/PredictiveScienceLab/py-mcmc) 631 | 632 | --- 633 | 634 | 635 | ## pymcmcstat 636 | 637 | > The pymcmcstat package is a Python program for running Markov Chain Monte Carlo (MCMC) simulations. Included in this package is the ability to use different Metropolis based sampling techniques: 638 | > 639 | > * Metropolis-Hastings (MH): Primary sampling method. 640 | > * Adaptive-Metropolis (AM): Adapts covariance matrix at specified intervals. 641 | > * Delayed-Rejection (DR): Delays rejection by sampling from a narrower distribution. Capable of n-stage delayed rejection. 642 | > * Delayed Rejection Adaptive Metropolis (DRAM): DR + AM 643 | > 644 | > This package is an adaptation of the MATLAB toolbox [mcmcstat](http://helios.fmi.fi/~lainema/mcmc/). 645 | 646 | [Repo]( 647 | https://github.com/prmiles/pymcmcstat) | 648 | [Docs]( 649 | https://pymcmcstat.readthedocs.io/en/latest/) | 650 | [Article]( 651 | https://joss.theoj.org/papers/10.21105/joss.01417) 652 | 653 | --- 654 | 655 | 656 | ## PyMultiNest 657 | 658 | > MultiNest is a program and a sampling technique. As a Bayesian inference 659 | technique, it allows parameter estimation and model selection. Recently, 660 | MultiNest added Importance Nested Sampling which is now also supported. 661 | > The efficient Monte Carlo algorithm for sampling the parameter space is based 662 | on nested sampling and the idea of disjoint multi-dimensional ellipse sampling. 663 | > For the scientific community, where Python is becoming the new lingua franca 664 | (luckily), I provide an interface to MultiNest. 665 | 666 | [Repo]( 667 | https://github.com/JohannesBuchner/PyMultiNest) | 668 | [Docs]( 669 | http://johannesbuchner.github.io/PyMultiNest/) | 670 | [Article]( 671 | http://www.aanda.org/articles/aa/abs/2014/04/aa22971-13/aa22971-13.html) 672 | 673 | --- 674 | 675 | 676 | ## pysmc 677 | 678 | > pysmc is a Python package for sampling complicated probability densities using the celebrated Sequential Monte Carlo method. 679 | 680 | [Repo]( 681 | https://github.com/PredictiveScienceLab/pysmc) | 682 | [Docs]( 683 | http://predictivesciencelab.github.io/pysmc/) 684 | 685 | --- 686 | 687 | 688 | ## PyStan 689 | 690 | > PyStan provides an interface to [Stan](http://mc-stan.org/), a package for 691 | Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian 692 | Monte Carlo. 693 | 694 | [Repo]( 695 | https://github.com/stan-dev/pystan) | 696 | [Docs]( 697 | https://pystan.readthedocs.io) 698 | 699 | --- 700 | 701 | 702 | ## Sampyl 703 | 704 | > Sampyl is a Python library implementing Markov Chain Monte Carlo (MCMC) 705 | samplers in Python. It’s designed for use in Bayesian parameter estimation 706 | and provides a collection of distribution log-likelihoods for use in 707 | constructing models. 708 | 709 | [Repo]( 710 | https://github.com/mcleonard/sampyl/) | 711 | [Docs]( 712 | http://matatat.org/sampyl/index.html) 713 | 714 | --- 715 | 716 | 717 | ## sbi 718 | 719 | > PyTorch package for simulation-based inference. Simulation-based inference is 720 | the process of finding parameters of a simulator from observations. sbi takes a Bayesian approach and returns a full posterior distribution over the parameters, conditional on the observations. This posterior can be amortized (i.e. useful for any observation) or focused (i.e. tailored to a particular observation), with different computational trade-offs. 721 | 722 | [Repo]( 723 | https://github.com/mackelab/sbi/) | 724 | [Docs]( 725 | https://www.mackelab.org/sbi/) | 726 | [Article]( 727 | https://doi.org/10.21105/joss.02505) 728 | 729 | --- 730 | 731 | 732 | ## simpleabc 733 | 734 | > A Python package for Approximate Bayesian Computation. 735 | 736 | [Repo]( 737 | https://github.com/rcmorehead/simpleabc) 738 | 739 | --- 740 | 741 | 742 | ## SPOTPY 743 | 744 | > A Statistical Parameter Optimization Tool for Python. 745 | > SPOTPY is a Python framework that enables the use of Computational 746 | optimization techniques for calibration, uncertainty and sensitivity analysis 747 | techniques of almost every (environmental-) model. 748 | 749 | [Repo]( 750 | https://github.com/thouska/spotpy) | 751 | [Docs]( 752 | http://fb09-pasig.umwelt.uni-giessen.de/spotpy/) | 753 | [Article]( 754 | https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0145180) 755 | 756 | --- 757 | 758 | 759 | ## UltraNest 760 | 761 | > UltraNest is intended for fitting complex physical models with slow likelihood 762 | evaluations, with one to hundreds of parameters. UltraNest intends to replace 763 | heuristic methods like multi-ellipsoid nested sampling and dynamic nested 764 | sampling with more rigorous methods. UltraNest also attempts to provide feature 765 | parity compared to other packages (such as MultiNest). 766 | 767 | [Repo]( 768 | https://github.com/JohannesBuchner/UltraNest) | 769 | [Docs]( 770 | https://johannesbuchner.github.io/UltraNest/index.html) | 771 | [Article]( 772 | https://link.springer.com/article/10.1007/s11222-014-9512-y) 773 | 774 | --- 775 | 776 | 777 | ## Zeus 778 | 779 | > zeus is a pure-Python implementation of the Ensemble Slice Sampling method. 780 | > 781 | > * Fast & Robust Bayesian Inference, 782 | > * No hand-tuning, 783 | > * Excellent performance in terms of autocorrelation time and convergence rate, 784 | > * Scale to multiple CPUs without any extra effort. 785 | 786 | [Repo]( 787 | https://github.com/minaskar/zeus) | 788 | [Docs]( 789 | https://zeus-mcmc.readthedocs.io/) | 790 | [Article]( 791 | https://api.semanticscholar.org/CorpusID:234338965) 792 | --------------------------------------------------------------------------------