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Parameter estimation for text analysis

: Heinrich, Gregor

Darmstadt, 2009, 32 S.
Fraunhofer IGD ()
machine learning; Monte Carlo; algorithm

Presents parameter estimation methods common with discrete probability distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed. As an application, the model of latent Dirichlet allocation (LDA) is explained in detail with a full derivation of an approximate inference algorithm based on Gibbs sampling, including a discussion of Dirichlet hyperparameter estimation. Finally, analysis methods of LDA models are discussed.