Bayesian inference using Gibbs sampling

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• Gibbs sampling — In statistics and in statistical physics, Gibbs sampling or a Gibbs sampler is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables. The purpose of such a sequence is to… …   Wikipedia

• Bayesian inference — is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. The name Bayesian comes from the frequent use of Bayes theorem in the inference process. Bayes theorem… …   Wikipedia

• OpenBUGS — Original author(s) Andrew Thomas Developer(s) OpenBUGS Foundation Initial release 2005 (2005) Stable release v3.2.1 / March 28, 2011 …   Wikipedia

• WinBUGS — is statistical software used for Bayesian modelling, developed by the MRC Biostatistics Unit, at the University of Cambridge, UK.It is based on the BUGS (Bayesian inference Using Gibbs Sampling) project started in 1989. It runs under Microsoft… …   Wikipedia

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• Latent Dirichlet allocation — In statistics, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups which explain why some parts of the data are similar. For example, if observations are words collected… …   Wikipedia

• Monte Carlo method — Not to be confused with Monte Carlo algorithm. Computational physics …   Wikipedia

• Markov chain Monte Carlo — MCMC redirects here. For the organization, see Malaysian Communications and Multimedia Commission. Markov chain Monte Carlo (MCMC) methods (which include random walk Monte Carlo methods) are a class of algorithms for sampling from probability… …   Wikipedia

• Mixture model — See also: Mixture distribution In statistics, a mixture model is a probabilistic model for representing the presence of sub populations within an overall population, without requiring that an observed data set should identify the sub population… …   Wikipedia

• Principle of maximum entropy — This article is about the probability theoretic principle. For the classifier in machine learning, see maximum entropy classifier. For other uses, see maximum entropy (disambiguation). Bayesian statistics Theory Bayesian probability Probability… …   Wikipedia

• Pattern theory — Pattern theory, formulated by Ulf Grenander, is a mathematical formalism to describe knowledge of the world as patterns. It differs from other approaches to artificial intelligence in that it does not begin by prescribing algorithms and machinery …   Wikipedia