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Using Distributions to make a Gibbs sampler Gibbs sampling is a statistical technique related to Monte Carlo Markov Chain sampling. It is used to search a solution space for an optimal (or at least locally optimal solution). It is an iterative technique. Basically, a single parameter is chosen at random and the value of it is set to a random value (or one chosen from a distribution).
JGibbLDA A Java Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for Parameter Estimation and Inference http://jgibblda.sourceforge.net/ Copyright © 2008 by Xuan-Hieu Phan (pxhieu at gmail dot com), Graduate School of Information Sciences, JGibbLDA: A Java Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for Parameter Estimation and Inference
“Gibbs Sampling for the Uninitiated” for the Uninitiated | Corner Cases Recently via Twitter I came across “Gibbs Sampling for the Uninitiated” by Philip Resnik and Eric Hardisty, a tutorial that shows how to use Gibbs sampling of a Naive Bayes model to estimate the labels on a set of documents. This paper goes through the algebra in great detail and concludes with pseudocode. Resnik and Hardisty do such a good job of making it look easy that I decided to write my own Gibbs sampler. It was, in fact, pretty easy.
README.md Naive Bayes Gibbs Sampler This project implements the Gibbs sampler for Naive Bayes document classification described in Resnik and Hardisty 2010, "Gibbs Sampling for the Uninitiated". It closely follows the notation and design put forth in that paper. There are only two significant differences between the algorithm presented in the paper and the code here. Unlike the paper, this code allows the documents to be grouped into more than two classes, so document priors are generated using a Dirichlet distribution rather than a Beta distribution. wpm/Naive-Bayes-Gibbs-Sampler - GitHub