Graphical Models, Exponential Families, and Variational Inference
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.
1 online resource
9781601981844, 1601981848
1116147768
1: Introduction 2: Background 3: Graphical models as exponential families 4: Sum product, Bethe-Kikuchi, and expectation-propagation 5: Mean field methods 6: Variational methods in parameter estimation 7: Convex relaxations and upper bounds 8: Max-product and LP relaxations 9: Moment matrices and conic relaxations 10: Discussion. A: Background Material B: Proofs for exponential families and duality C: Variational principles for multivariate Gaussians D: Clustering and augmented hypergraphs E: Miscellaneous results References
20171215.