'Likelihood score' diaporamas de présentation

LING 696B: Mixture model and linear dimension reduction

707 views • 58 slides

Structure Learning in Bayesian Networks

Structure Learning in Bayesian Networks. Eran Segal Weizmann Institute. Structure Learning Motivation. Network structure is often unknown Purposes of structure learning Discover the dependency structure of the domain

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The Hybrid CART-Logit Model in Classification and Data Mining

The Hybrid CART-Logit Model in Classification and Data Mining. Dan Steinberg N. Scott Cardell dstein@salford-systems.com http://www.salford-systems.com. Data mining. Attempt to discover possibly very complex structure in huge databases (large number of records and large number of variables)

547 views • 38 slides

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BNFO 602 Phylogenetics

BNFO 602 Phylogenetics. Usman Roshan. Maximum Likelihood. D = data, M = model Bayes rule P(M|D) = P(D|M)P(M) / P(D) P(M|D) is the posterior probability P(D|M) is the likelihood P(M) is the prior probability on the model By rewriting P(D) we get = P(D|M)P(M) / ? M P(D|M)P(M)

143 views • 6 slides