Graphical Models for Machine Learning and Digital Communication

Adaptive Computation and Machine Learning

by Brendan J. Frey

A variety of problems in machine learning and digital communication deal with complex
but structured natural or artificial systems. In this book, Brendan Frey uses graphical models as an
overarching framework to describe and solve problems of pattern classification, unsupervised
learning, data compression, and channel coding. Using probabilistic structures such as Bayesian
belief networks and Markov random fields, he is able to describe the relationships between random
variables 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 iterative
turbodecoding algorithm (currently the best error-correcting decoding algorithm), the bits-back
coding method, the Markov chain Monte Carlo technique, and variational inference.

216 pages, Kindle Edition

First published January 1, 1998