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Infer.NET is a free and open source.NET software library for machine learning. [2] It supports running Bayesian inference in graphical models and can also be used for probabilistic programming . [ 3 ]
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 . Versions from v3.0.7 onwards have been designed to be at least as efficient and reliable as WinBUGS over a range of test applications.
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 ...
Engine Front-end/ installer License External access Blind solving 360° (off line) Cloud access to nova.astrometry.net MS-Windows (X86) Linux (X86) Linux (ARM)
mcmc-jags.sourceforge.net 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.
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 ...
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.
Stan (software) – open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It is somewhat like BUGS, but with a different language for expressing models and a different sampler for sampling from their posteriors; Statistical Lab – R-based and focusing on educational purposes