• BUGS

  • Referenced in 299 articles [sw07885]
  • with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte...
  • Stan

  • Referenced in 102 articles [sw10200]
  • probabilistic programming language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized...
  • PMTK

  • Referenced in 91 articles [sw14689]
  • encompassing machine learning, graphical models, and Bayesian statistics (hence the logo). (Some methods from frequentist...
  • bayesm

  • Referenced in 43 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 23 articles [sw13990]
  • probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough...
  • PyMC

  • Referenced in 19 articles [sw10482]
  • python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo...
  • OpenBUGS

  • Referenced in 62 articles [sw08316]
  • performing Bayesian inference Using Gibbs Sampling. The user specifies a statistical model, of (almost) arbitrary...
  • boa

  • Referenced in 72 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...
  • spBayes

  • Referenced in 254 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...
  • Infer.NET

  • Referenced in 26 articles [sw07886]
  • message-passing algorithms and statistical routines for performing Bayesian inference. It has applications...
  • Edward

  • Referenced in 9 articles [sw21517]
  • data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic...
  • rstan

  • Referenced in 8 articles [sw16103]
  • probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough...
  • Bolstad

  • Referenced in 8 articles [sw11019]
  • sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2007), John Wiley & Sons ISBN...
  • Mcmcpack

  • Referenced in 38 articles [sw07974]
  • perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation...
  • Bolstad2

  • Referenced in 7 articles [sw11020]
  • sets for the book Understanding Computational Bayesian Statistics, Bolstad, W.M. (2009), John Wiley & Sons ISBN...
  • 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...
  • BayesPeak

  • Referenced in 5 articles [sw18843]
  • model the data structure using Bayesian statistical techniques and was shown to be a reliable...
  • PROC MCMC

  • Referenced in 2 articles [sw12072]
  • designed to fit Bayesian models. Bayesian statistics is different from traditional statistical methods such ... varying degrees of difficulty. In essence, Bayesian statistics treats parameters as unknown random variables ... several advantages associated with this approach to statistical inference. Some of the advantages include ... relative advantages and disadvantages of Bayesian analysis, see the section Bayesian Analysis: Advantages and Disadvantages...
  • LearnBayes

  • Referenced in 3 articles [sw04495]
  • learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic ... contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling...