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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.
JGibbLDA A Java Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for Parameter Estimation and Inference Copyright © 2008 by Xuan-Hieu Phan (pxhieu at gmail dot com), Graduate School of Information Sciences,
PS_cache/arxiv/pdf/1107/1107.3765v1.pdf. “Gibbs Sampling for the Uninitiated” for the Uninitiated. 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. Wpm/Naive-Bayes-Gibbs-Sampler - GitHub.