Graphical Models for Machine Learning and Digital Communication (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)

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.