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Free and open-source software portal; Pages in category "Free Bayesian statistics software" The following 9 pages are in this category, out of 9 total.
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.
Bain: Bayesian informative hypotheses evaluation [8] for t-tests, ANOVA, ANCOVA, linear regression and structural equation modeling. BSTS: Bayesian take on linear Gaussian state space models suitable for time series analysis. Circular Statistics: Basic methods for directional data. Cochrane meta-analyses: Analyse Cochrane medical datasets.
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.
Just another Gibbs sampler (JAGS) is a program for simulation from Bayesian hierarchical models using Markov chain Monte Carlo (MCMC), developed by Martyn Plummer. JAGS has been employed for statistical work in many fields, for example ecology, management, and genetics.
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]
Free software portal; Mathematics portal; 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 mode
LaplacesDemon is an open-source statistical package that is intended to provide a complete environment for Bayesian inference. LaplacesDemon has been used in numerous fields. [1] [2] [3] The user writes their own model
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