• Stan

  • Referenced in 150 articles [sw10200]
  • probabilistic programming language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized...
  • PMTK

  • Referenced in 143 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 likelihood-based 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 38 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 67 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 menu-driven 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]
  • message-passing algorithms and statistical routines for performing Bayesian inference. It has applications...
  • Mcmcpack

  • Referenced in 46 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 bottom-up saliency emerges ... existing saliency measures, which depend on the statistics of the particular image being viewed...