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2008

Graphical Models, Exponential Families, and Variational Inference

8 years 10 months ago
Graphical Models, Exponential Families, and Variational Inference
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, we develop general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations. We describe how a wide varie...
Martin J. Wainwright, Michael I. Jordan
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where FTML
Authors Martin J. Wainwright, Michael I. Jordan
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