Handbook of Graphical Models (Hardcover)

Introduction. Part I - Conditional independencies and Markov properties; 1. Conditional independence; 2. Basic Markov properties for undirected and directed graphs; 3. Markov properties for mixed graphs; 4. Algebraic properties of conditional independence.

Part II - Computing with factorizing distributions; 5. Algorithms and data structures for exact computation of marginal; 6. Approximate methods for calculating marginals and likelihoods; 7. MAP estimation: linear programming relaxation, messagepassing algorithms; 8. Sequential and other Monte Carlo methods; 9. Graphical models for nonparametric Bayesian inference.

Part III - Statistical inference; 10. Discrete graphical models; 11. Likelihood theory of Gaussian graphical models; 12. Bayesian inference in Gaussian models; 13. Latent tree models; 14. Neighborhood selection methods; 15. Non- and semi-parametric graphical models; 16. Constraint-based and score-based methods for graph identification; 17. Restricted structural equation models; 18. Graphical models for time series; 19. Inference in high-dimensional graphical models.

Part IV - Causal inference; 20. Fundamental causal concepts for graphical models; 21. Identification of causal effects in nonparametric and linear structural equation models; 22. Mediation analysis; 23. Graphical models and potential outcomes. Part V ? Applications; 24. Graphical models for error-control coding; 25. Graphical models for decision analysis and expert systems; 26. Graphical models for forensic analysis; 27. Graphical models for bioinformatics I; 28. Graphical models for bioinformatics II.