Variational Bayesian methods
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Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They are typically used in complex statistical model
Pictorial illustration of coordinate ascent variational inference algorithm by the duality formula
Gibbs sampling
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In statistics, Gibbs sampling or a Gibbs sampler, also known in statistical mechanics as the heat bath algorithm, is a Markov chain Monte Carlo algorithm for sampling from a specified multivariate pro
Variational Bayesian methods
…a posterior probability), variational Bayes is an alternative to Monte Carlo sampling methods—particularly, Markov chain Monte Carlo methods such as Gibbs sampling—for taking a fully Bayesian approach to statistical inference over complex distributions that are difficult to evaluate directly or sample. In particular…
A pictorial description of the Gibbs sampling algorithm
Schematic description of the information equality associated with the Gibbs sampler at the i-th step within a cycle