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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 ...
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.
Neighbourhood components analysis is a supervised learning method for classifying multivariate data into distinct classes according to a given distance metric over the data. . Functionally, it serves the same purposes as the K-nearest neighbors algorithm and makes direct use of a related concept termed stochastic nearest neighbo
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.
In a Bayesian network, the Markov boundary of node A includes its parents, children and the other parents of all of its children.. In statistics and machine learning, when one wants to infer a random variable with a set of variables, usually a subset is enough, and other variables are useless.
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often ...
tensorBF [31] is an R package for Bayesian Tensor decomposition. MTF [ 32 ] Bayesian Multi-Tensor Factorization for data fusion and Bayesian versions of Tensor PCA and Tensor CCA. Software: MTF .
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