Graphical Models, Exponential Families, and Variational Inference (Foundations and Trends (R) in Machine Learning): 1 - Brossura
Sinossi
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning.
Many problems that arise in specific instances-including the key problems of computing marginals and modes of probability distributions-are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, this book develops general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations. It describes how a wide variety of algorithms- among them sum-product, cluster variational methods, expectation-propagation, mean field methods, and max-product-can all be understood in terms of exact or approximate forms of these variational representations.
The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.
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Book by Wainwright Martin J Jordan Michael I
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Graphical Models, Exponential Families, and Variational Inference (Foundations and Trends(r) in Machine Learning)
Editore:
Now Publishers Inc, 2008
ISBN 10: 1601981848
ISBN 13: 9781601981844
Antico o usato
Paperback
Da: Mispah books, Redhill, SURRE, Regno Unito
Valutazione del venditore 4 su 5 stelle
Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Codice articolo ERICA77316019818486
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EUR 286,92
Spedizione EUR 28,66
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
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