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OpenBUGS is a software application for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. OpenBUGS is the open source variant of WinBUGS (Bayesian inference Using Gibbs Sampling). It runs under Microsoft Windows and Linux, as well as from inside the R statistical package.
Bambi is a high-level Bayesian model-building interface written in Python.It works with the PyMC probabilistic programming framework. Bambi provides an interface to build and solve Bayesian generalized (non-)linear multivariate multilevel models.
gretl is an example of an open-source statistical package. ADaMSoft – a generalized statistical software with data mining algorithms and methods for data management; ADMB – a software suite for non-linear statistical modeling based on C++ which uses automatic differentiation; Chronux – for neurobiological time series data; DAP – free ...
Stan: A probabilistic programming language for Bayesian inference and optimization, Journal of Educational and Behavioral Statistics. Hoffman, Matthew D., Bob Carpenter, and Andrew Gelman (2012). Stan, scalable software for Bayesian modeling Archived 2015-01-21 at the Wayback Machine, Proceedings of the NIPS Workshop on Probabilistic Programming.
Bayesian inference using Gibbs sampling (BUGS) is a statistical software for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods. It was developed by David Spiegelhalter at the Medical Research Council Biostatistics Unit in Cambridge in 1989 and released as free software in 1991.
WinBUGS is statistical software for Bayesian analysis using Markov chain Monte Carlo (MCMC) methods. It is based on the BUGS (Bayesian inference Using Gibbs Sampling) project started in 1989. It runs under Microsoft Windows, though it can also be run on Linux or Mac using Wine. [1]
Distributions: Visualise probability distributions and fit them to data. Equivalence T-Tests: Test the difference between two means with an interval-null hypothesis. JAGS: Implement Bayesian models with the JAGS program for Markov chain Monte Carlo. Learn Bayes: Learn Bayesian statistics with simple examples and supporting text.
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