Graphical Models for Machine Learning and Digital Communication

A variety of problems in machine learning and digital communication deal with complexbut structured natural or artificial systems. In this book, Brendan Frey uses graphical models as anoverarching framework to describe and solve problems of pattern classification, unsupervisedlearning, data compression, and channel coding. Using probabilistic structures such as Bayesianbelief networks and Markov random fields, he is able to describe the relationships between randomvariables in these systems and to apply graph-based inference techniques to develop new algorithms.Among the algorithms described are the wake-sleep algorithm for unsupervised learning, the iterativeturbodecoding algorithm (currently the best error-correcting decoding algorithm), the bits-backcoding method, the Markov chain Monte Carlo technique, and variational inference.