Modeling and Reasoning with Bayesian Networks Book

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.