Graphical Models For Machine Learning And Digital Communication

A variety of problems in machine learning and digital communication dealwith complex but structured natural or artificial systems. In this book, BrendanFrey uses graphical models as an overarching framework to describe and solveproblems of pattern classification, unsupervised learning, data compression, andchannel coding. Using probabilistic structures such as Bayesian belief networks andMarkov random fields, he is able to describe the relationships between randomvariables in these systems and to apply graph-based inference techniques to developnew algorithms. Among the algorithms described are the wake-sleep algorithm forunsupervised learning, the iterative turbodecoding algorithm (currently the besterror-correcting decoding algorithm), the bits-back coding method, the Markov chainMonte Carlo technique, and variational inference.