├── LICENSE ├── README.md └── bda.csv /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Blaine Mooers and The University of Oklahoma Board of Regents 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Version](https://img.shields.io/static/v1?label=bayesian-data-analysis-voice-in&message=0.2&color=brightcolor) 2 | [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) 3 | 4 | # Voice In commands related to Bayesian Data Analysis 5 | 6 | ## Introduction 7 | This repo contains voice commands about Bayesian Data Analysis (BDA) for the automated speech recognition (ASR) software Voice In Plus. 8 | The utilization of these custom commands requires a subscription to Voice In Plus. 9 | These commands can be used in the text area of most websites opened in Google Chrome or Microsoft Edge. 10 | 11 | They can be used during the dictation of manuscripts about Bayesian data analysis. 12 | Some of the commands include equations that are represented as LaTeX math. 13 | These short snippets of LaTeX are recognized and rendered correctly by most typesetting languages, including org-mode, an enhanced form of Markdown best used inside of Emacs but available in VS Code and elsewhere. 14 | 15 | ## Usage 16 | You can use the commands right away after they have been uploaded. 17 | I toggle Voice In on and off by using a keyboard shortcut. 18 | I then dictate the command. 19 | See the Voice In plug-in documentation to learn how to configure keyboard shortcuts. 20 | 21 | ## Installation 22 | Each command is paired with the inserted text on a single line in a comma-separated value file (**bda.csv**). 23 | Use the **bulk add** button in Voice In Plus to upload these commands into your collection of custom commands. 24 | 25 | ## Planned content of the library bda.csv 26 | - Acronyms and their expansions 27 | - Key equations typeset in LaTeX. 28 | - URLs of critical websites. 29 | - Names of key people in the field to ensure the correct spelling of their names. 30 | 31 | ## Related repositories 32 | See [Voice Computing section of landing page](https://github.com/MooersLab/MooersLab?tab=readme-ov-file#voice-computing) 33 | 34 | ## Rules for developing voice commands 35 | 36 | ### Pick word combinations rarely used in ordinary prose 37 | The basic rule for developing a voice command is to pick a word combination that is very unlikely to be used in one's prose. 38 | This choice can avoid the accidental insertion of an unintended set of words. 39 | 40 | ### Pick word combinations that do not contain other commands 41 | If you pick a word combination with a subset of words already assigned to another command, the commands will collide, and you will not get the intended effect. 42 | It is better to pick a synonym for the new command than include the old one. 43 | 44 | ### Use verbs are prefaces 45 | - `insert` before name for the computer code to insert. 46 | - `expand` to expand acronyms. 47 | - `url` to insert a URL. 48 | - `open` to open a specific website. 49 | - `display` for equations in the display mode. 50 | - `inline` for inline equations in sentences. 51 | 52 | ## Status 53 | Ready to use but still under development. 54 | 55 | ## Update History 56 | 57 | |Version | Changes | Date | 58 | |:-----------:|:-----------------------------------------------:|:---------------:| 59 | | Version 0.2 | Fixed typos in README.md | 2024 April 10 | 60 | | Version 0.3 | Updated the README.md. Added some terms to the csv file. | 2024 Juen 4 | 61 | 62 | 63 | 64 | ## Sources of funding 65 | 66 | - NIH: R01 CA242845 67 | - NIH: R01 AI088011 68 | - NIH: P30 CA225520 (PI: R. Mannel) 69 | - NIH P20GM103640 and P30GM145423 (PI: A. West) 70 | -------------------------------------------------------------------------------- /bda.csv: -------------------------------------------------------------------------------- 1 | expand ACE,Approximate Coordinate Exchange 2 | expand ADVI,Automatic Differentiation Variational Inference 3 | expand AIC,Akaile Information Criterion 4 | expand AICc,Akaile Information Criterion corrected for small sample size10! 5 | expand AIS,adaptive importance sampling 6 | expand AMCI,Amortized Monte Carlo Integration 7 | expand ANN,artifical neural network 8 | expand ANN-DOE,artifical neural network design of experiments 9 | expand BBD,Box-Behnken design 10 | expand BDA,Bayesian Data Analysis 11 | expand BEST,Bayesian Estimation Supercedes the T-test 12 | expand BO,Bayeisan optimization 13 | expand BOED,Bayesian optimal experimental design 14 | expand BHMC,Barrier Hamiltonian Monte Carlo 15 | expand BIC,Bayesian Information Criterion 16 | expand BN,Bayesian Network 17 | expand BP,Belief Propagation 18 | expand BUGS,Bayesian inference Using Gibbs Sampling 19 | expand cBHMC,continuous Barrier Hamiltonian Monte Carlo 20 | expand CRI,Credible Interval 21 | expand DQMC,Determinant Quantum Monte Carlo 22 | expand DBN,Dynamic Bayesian Network 23 | expand DIC,Deviance Information Criterion 24 | expand DLGM,Deep Latent Gaussian Model 25 | expand DOE,Design of Experiments 26 | expand DTL cycle,"design, test, and learn cycle" 27 | expand EAP,Expected a posterior 28 | expand EIG,Expected Information Gain 29 | expand EHVI,expected hypervolume improvement 30 | expand ELBO,Evidence Lower Bound 31 | expand eLLGI,estimated Log-likelihood Gain on the Intensities 32 | expand EM,Expectation Maximization 33 | expand GAM,Generalized Additive Models 34 | expand GEE,Generalized Esimating Equations 35 | expand GLMs10,General Linear Models 36 | expand GLMM,General Linear Mixed Models 37 | expand GMRF,Gaussian Markov Random Field 38 | expand GMM,Generalized Methods of Moments 39 | expand GP,Gaussian Processes 40 | expand GPR,Gaussian Process Regression 41 | expand GTI,generalized thermodynamic integration 42 | expand HDMR,High Dimensional Model Representation 43 | expand HM,Hierarchical Model 44 | expand HM,harmonic mean 45 | expand HMC,Hamiltonian Monte Carlo or Hybrid MC 46 | expand HMM,Hidden Markov Model 47 | expand HPDI,Highest Posterior Density Interval 48 | expand IFE,Incomplete Factorial Experiments 49 | expand IMSE,Integrated Mean Squared Error 50 | expand INLA,Integrated Nested Laplace Approximation 51 | expand IAF,Inverse Autoregressive Flows 52 | expand IW,Inverse Wishart distribution 53 | expand IRLS,Iterative Re-Weighted Least-Squares 54 | expand IS,importance sampling 55 | expand ISBA,International Society for Bayesian Analysis 56 | expand IWLS,Iterative Weighted Least-Squares 57 | expand JAGS,Just Another Gibbs Sampler 58 | expand KDE,kernal density estimation 59 | expand KL,Kullback-Leibler 60 | expand LASSO,least absobute shrinkage and selection operator 61 | expand LHS,Latin hypercube sampling 62 | expand L-BFGS,limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer 63 | expand LHD,Latin HyperCube Design 64 | expand LM-BFGS,limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer 65 | expand LM,Linear Models 66 | expand LMM,Linear Mixed Models 67 | expand LKJ,LKJ Distribution 68 | expand LLGI,log-likelihood Gain on the Intensities 69 | expand LOO,Leave-One-Out 70 | expand LSTM,Long-Short-Term-Memory 71 | expand MALA,Metropolis adjusted Langevin algorithms 72 | expand MAP,Maximum aposterior 73 | expand MaxMinDist sampler,Maximized Minimal Distance Sampler 74 | expand MAXS,Medium Angle X-ray Scattering 75 | expand MCEM,Monte Carlo Expectation Maximization 76 | expand MCMC,Markov Chain Monte Carlo 77 | expand MH,Metropolis-Hastings 78 | expand ML,Maximum Likelihood 79 | expand MLE,Maximum Likelihood Estimate 80 | expand MRF,Markov Random Field 81 | expand MLIRT,multilevel item response theory 82 | expand MOO,Multiobjective Optimization 83 | expand MUM-PCE,Method of Uncertainty Minimization using Polynomial Chaos 84 | expand NB,negative binomial 85 | expand NB1,negative binomial model 1 86 | expand NB2,negative binomial model 2 87 | expand NeuTra HMC,Neural Transport HMC 88 | expand NIMBLE,Numerical Inference Models Bayesian Likelihood Estimation 89 | expand NN,neural networks 90 | expand NN-RS,neural network response surfaces 91 | expand NUTS,No U-Turn Sampler 92 | expand OBsMD,Objective Bayesian Model Discrimination 93 | expand ODBC,Open Database Connectivity 94 | expand ODE,ordinary differential equations 95 | expand OFAT,one-factor-at-a-time 96 | expand OLS,Ordinary Least Squares 97 | expand OMD,Objective Model Descrimination 98 | expand PB,Plackett and Burman design 99 | expand PDF,Probability Density Function 100 | expand pMCMC,particle MCMC 101 | expand pcc,Probabilistic Cross-Categorization 102 | expand PINNS,Physics Informed Neural Networks 103 | expand PPC,Posterior Predictive Checking 104 | expand PPL,Probabilistic Programming Language 105 | expand PP,power posteriors 106 | expand PS,path sampling 107 | expand PSIS-LOO-CV,Pareto-smoothed importance sampling leave-one-out cross-validation 108 | expand QMC,Quasi-Monte Carlo 109 | expand QML,quantum machine learning 110 | expand QSSA,quasi-steady-state assumption 111 | expand RDBMS,Relational Database Management System 112 | expand ReLU,rectified linear unit 113 | expand REST,Representational State Transfer 114 | expand RMSE,root-mean-square error 115 | expand RMHMC,Riemannian manifold Hamiltonian Monte Carlo 116 | expand RNN,recurrent neural network 117 | expand RIS,Reverse Importance Sampling 118 | expand RJ-MCMC,Reverse Jump-MCMC 119 | expand RQMC,Randomized Quasi-Monte Carlo 120 | expand RSM,Response Surface Methods 121 | expand SIAM,Soceity for Industrial and Applied Mathematics 122 | expand SMC,sequential Monte Carlo 123 | expand SOAP,Simple Object Access Processing 124 | expand SEM,Structural Equation Modeling 125 | expand SG-MCMC,Stochastic gradient Markov chain Monte Carlo 126 | expand SLS,standard least squares 127 | expand SLURM,Simple Linux Utility for Resource Management 128 | expand SMS,Sparse Matrix Sampling 129 | expand SQL,Structured query language 130 | expand TABI,target-aware Bayesian inference 131 | expand TI,thermodynamic integration 132 | expand UM,uncertainty minimization 133 | expand UML,Unified Modeling Language 134 | expand UQ,uncertainty quantification 135 | expand WAIC,widely applicable information criterion 136 | expand VAEs,Variational Autoencoders 137 | expand VGAM,Vector Generalizer Additive Model 138 | expand VGLM,Vector Generalizer Lienar Model 139 | expand AIC,Watanabe-Akaile Information Criterion 140 | expand XML,Extensible Markup Language 141 | expand ZIP,Zero-Inflated Negative Binomial distribution 142 | expand ZIP,Zero-Inflated Poisson distribution 143 | expand ZTNB,Zero-Truncated Negative Binomial distribution 144 | expand ZTP,Zero-Truncated Poisson distribution 145 | bridge stan,BridgeStan 146 | nut pie,nutpy 147 | open bayesian modeling and computation in Python, 148 | open bridge stan, 149 | open bridge stan documentation, 150 | open ISBA YouTube channel, 151 | open JASP, 152 | open JASP download, 153 | open JointProb, 154 | open lace github, 155 | open github lace, 156 | open github Bayesian data analysis with Python, 157 | open pymc, 158 | open rethinking, 159 | open rethinking github, 160 | open real world data, 161 | open scicloj jointprob, 162 | open scicloj real world data, 163 | open stan mc, 164 | open stan users guide, 165 | open stan reference manual, 166 | open statistical thinking for the 21st century, 167 | open surrogate modeling, 168 | 169 | --------------------------------------------------------------------------------