Graphical Models for Machine Learning and Digital Communication


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.

Table of Contents

  1. Series Forward
  2. Preface
  3. 1. Introduction
  4. 2. Probabilistic Inference in Graphical Models
  5. 3. Pattern Classification
  6. 4. Unsupervised Learning
  7. 5. Data Compression
  8. 6. Channel Coding
  9. 7. Future Research Directions
  10. References
  11. Index