
Stan
 Referenced in 151 articles
[sw10200]
 probabilistic programming language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized...

PMTK
 Referenced in 146 articles
[sw14689]
 encompassing machine learning, graphical models, and Bayesian statistics (hence the logo). (Some methods from frequentist...

BUGS
 Referenced in 334 articles
[sw07885]
 with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte...

bayesm
 Referenced in 47 articles
[sw06787]
 Teaching Bayesian statistics to marketing and business students. We discuss our experiences teaching Bayesian statistics ... students often have weak backgrounds in mathematical statistics and a predisposition against likelihoodbased methods ... course that emphasizes the value of the Bayesian approach to solving nontrivial problems. The success ... primarily due to the emphasis on statistical computing. This is facilitated by our R package...

RStan
 Referenced in 39 articles
[sw13990]
 probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough...

PyMC
 Referenced in 26 articles
[sw10482]
 python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo...

rstan
 Referenced in 15 articles
[sw16103]
 probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough...

Edward
 Referenced in 14 articles
[sw21517]
 data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic...

Bolstad
 Referenced in 12 articles
[sw11019]
 sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2007), John Wiley & Sons ISBN...

OpenBUGS
 Referenced in 68 articles
[sw08316]
 performing Bayesian inference Using Gibbs Sampling. The user specifies a statistical model, of (almost) arbitrary...

spBayes
 Referenced in 296 articles
[sw10160]
 geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves ... such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical...

boa
 Referenced in 82 articles
[sw04493]
 package boa: Bayesian Output Analysis Program (BOA) for MCMC. A menudriven program and library ... functions for carrying out convergence diagnostics and statistical and graphical analysis of Markov chain Monte...

Infer.NET
 Referenced in 27 articles
[sw07886]
 messagepassing algorithms and statistical routines for performing Bayesian inference. It has applications...

Mcmcpack
 Referenced in 47 articles
[sw07974]
 perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation...

Bolstad2
 Referenced in 9 articles
[sw11020]
 sets for the book Understanding Computational Bayesian Statistics, Bolstad, W.M. (2009), John Wiley & Sons ISBN...

BayesPeak
 Referenced in 7 articles
[sw18843]
 model the data structure using Bayesian statistical techniques and was shown to be a reliable...

cudaBayesreg
 Referenced in 6 articles
[sw24712]
 package provides a CUDA implementation of a Bayesian multilevel model for the analysis of brain ... processed, and the type of statistical analysis to perform in fMRI analysis, call for high ... each voxel in parallel. The global statistical model implements a Gibbs Sampler for hierarchical linear ... Rossi, Allenby and McCulloch in ‘Bayesian Statistics and Marketing’, Chapter 3, and is referred...

Grapham
 Referenced in 5 articles
[sw08541]
 applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementation...

BayesTree
 Referenced in 57 articles
[sw07995]
 posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis ... particular, BART is defined by a statistical model: a prior and a likelihood. This approach...

SUN
 Referenced in 12 articles
[sw28156]
 Bayesian framework for saliency using natural statistics. We propose a definition of saliency by considering ... directing attention. The resulting model is a Bayesian framework from which bottomup saliency emerges ... existing saliency measures, which depend on the statistics of the particular image being viewed...