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A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). [1] While it is one of several forms of causal notation, causal networks are special cases of Bayesian ...
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.
bnt on GitHub: the Bayes Net Toolbox for Matlab, by Kevin Murphy, (released under a GPL license) Graphical Models Toolkit (GMTK): an open-source, publicly available toolkit for rapidly prototyping statistical models using dynamic graphical models (DGMs) and dynamic Bayesian networks (DBNs). GMTK can be used for applications and research in ...
Originally intended to solve problems encountered in medical statistics, it soon became widely used in other disciplines, such as ecology, sociology, and geology. [2] The last version of WinBUGS was version 1.4.3, released in August 2007. Development is now focused on OpenBUGS, an open-source version of the package. WinBUGS 1.4.3 remains ...
CmdStanR and rstan – R software libraries, CmdStanPy and PyStan – libraries for the Python programming language, CmdStan.rb - library for the Ruby programming language, MatlabStan – integration with the MATLAB numerical computing environment, Stan.jl – integration with the Julia programming language, StataStan – integration with Stata.
Bayesian programming is a formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available. Edwin T. Jaynes proposed that probability could be considered as an alternative and an extension of logic for rational reasoning with incomplete and uncertain ...
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