Graphical models for machine learning and digital communication

Summary of contents

• Probabilistic inference in graphical models
• pattern classification
• unsupervised learning
• data compression
• channel coding
• future research directions

Table of contents

Series Foreword

Preface

1 Introduction

1.1 A probabilistic perspective

1.2 Graphical models: Factor graphs, Markov random fields and Bayesian belief networks

1.3 Organization of this book

2 Probabilistic Inference in Graphical Models

2.1 Exact inference using probability propagation (the sum-product algorithm)

2.2 Monte Carlo inference: Gibbs sampling and slice sampling

2.3 Variational inference

2.4 Helmholtz machines

3 Pattern Classification

3.1 Bayesian networks for pattern classification

3.2 Autoregressive networks

3.3 Estimating latent variable models using the EM algorithm

3.4 Multiple-cause networks

3.5 Classification of handwritten digits

4 Unsupervised Learning

4.1 Extracting structure from images using the wake-sleep algorithm

4.2 Simultaneous extraction of continuous and categorical structure

4.3 Nonlinear Gaussian Bayesian networks (NLGBNs)

5 Data Compression

5.1 Fast compression with Bayesian networks

5.2 Communicating extra information through the codeword choice

5.3 Relationship to maximum likelihood estimation

5.4 The "bits-back" coding algorithm

5.5 Experimental results

5.6 Integrating over model parameters using bits-back coding

6 Channel Coding

6.1 Review: Simplifying the playing field

6.2 Graphical models for error correction: Turbocodes, low-density parity-check codes and more

6.3 "A code by any other network would not decode as sweetly"

6.4 Trellis-contrained codes (TCCs)

6.5 Decoding complexity of iterative decoders

6.6 Parallel iterative decoding

6.7 Speeding up iterative decoding by detecting variables early

7 Future Research Directions

7.1 Modularity and abstraction

7.2 Faster inference and learning

7.3 Scaling up to the brain

7.4 Improving model structures

7.5 Iterative decoding

7.6 Iterative decoding in the real world

7.7 Unification

References

Index