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Parameter estimation for text analysis
Parameter estimation for text analysisGregor HeinrichTechnical Notevsonix GmbH+University of Leipzig, Parameter estimation methods common with discrete proba-bility distributions, which is of particular interest in text modeling. Starting withmaximum likelihood, a posteriori and Bayesian estimation , central concepts likeconjugate distributions and Bayesian networks are reviewed. As an application,the model of latent Dirichlet allocation (LDA) is explained in detail with a fullderivation of an approximate inference algorithm based on Gibbs sampling, in-cluding a discussion of Dirichlet hyperparameter :version 1: May 2005, version : August IntroductionThis technical note is intended to review the foundations of Bayesian Parameter esti-mation in the discrete domain, which is necessary to understand the inner workings oftopic-based text analysis approaches like probabilistic latent semantic analysis (PLSA)[Hofm99], latent Dirichlet allocation (LDA) [BNJ02] and other mixture models ofcount data.
Parameter estimation for text analysis Gregor Heinrich Technical Note vsonix GmbH + University of Leipzig, Germany gregor@vsonix.com Abstract. Presents parameter estimation methods common with discrete proba-
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