Darwiche A. Modeling and Reasoning with Bayesian Networks

Cambridge University Press, 2009. — 548 p.
ISBN: 978-0521884389, e-ISBN: 978-0511507281.
This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.Propositional Logic
Probability Calculus
Bayesian Networks
Building Bayesian Networks
Inference by Variable Elimination
Inference by Factor Elimination
Inference by Conditioning
Models for Graph Decomposition
Most Likely Instantiations
The Complexity of Probabilistic Inference
Compiling Bayesian Networks
Inference with Local Structure
Approximate Inference by Belief Propagation
Approximate Inference by Stochastic Sampling
Sensitivity Analysis
Learning: The Maximum Likelihood Approach
Learning: The Bayesian Approach
A Notation
B Concepts from Information Theory
C Fixed Point Iterative Methods
D Constrained Optimization