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 treatment of exact algorithms covers the main
inference paradigms based on elimination and conditioning and
includes advanced methods for compiling Bayesian networks,
time-space tradeoffs, and exploiting local structure of massively
connected networks. The treatment of approximate algorithms covers
the main inference paradigms based on sampling and optimization and
includes influential algorithms such as importance sampling, MCMC,
and belief propagation. 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.
目錄：
1. Introduction
2. Propositional logic
3. Probability calculus
4. Bayesian networks
5. Building Bayesian networks
6. Inference by variable elimination
7. Inference by factor elimination
8. Inference by conditioning
9. Models for graph decomposition
10. Most likely instantiations
11. The complexity of probabilistic inference
12. Compiling Bayesian networks
13. Inference with local structure
14. Approximate inference by belief propagation
15. Approximate inference by stochastic sampling
16. Sensitivity analysis
17. Learning: the maximum likelihood approach
18. Learning: the Bayesian approach
Appendix A: notation
Appendix B: concepts from information theory
Appendix C: fixed point iterative methods
Appendix D: constrained optimization
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