Parameter estimation for text analysis
نویسنده
چکیده
This primer presents parameter estimation methods common in Bayesian statistics and apply them to discrete probability distributions, which commonly occur in text modeling. Presentation starts with maximum likelihood and a posteriori estimation approaches and the full Bayesian approach. This presentation is completed by an overview of Bayesian networks, a graphical language to express probabilistic models. As an application, the model of latent Dirichlet allocation is explained and a full derivation of an approximate inference algorithm given based on Gibbs sampling.
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