Part I: Conditional independencies and Markov properties.
1. Conditional Independence and Basic Markov Properties - Milan Studeny
2. Markov Properties for Mixed Graphical Models - Robin Evans
3. Algebraic Aspects of Conditional Independence and Graphical Models - Thomas Kahle, Johannes Rauh, and Seth Sullivant
Part II: Computing with factorizing distributions
4. Algorithms and Data Structures for Exact Computation of Marginals - Jeffrey A. Bilmes
5. Approximate Methods for Calculating Marginals and Likelihoods - Nicholas Ruozzi
6. MAP Estimation: Linear Programming Relaxation and Message-Passing Algorithms - Ofer Meshi and Alexander G. Schwing
7. Sequential Monte Carlo Methods - Arnaud Doucet and Anthony Lee
Part III: Statistical inference
8. Discrete Graphical Models and their Parametrization - Luca La Rocca and Alberto Roverato
9. Gaussian Graphical Models - Caroline Uhler
10. Bayesian Inference in Graphical Gaussian Models - Helene Massam
11. Latent Tree Models - Piotr Zwiernik
12.Neighborhood Selection Methods - Po-Ling Loh
12. Nonparametric Graphical Models - Han Liu and John Lafferty
14. Inference in High-Dimensional Graphical Models - Jana Jankova and Sara van de Geer
Part IV: Causal inference
15. Causal Concepts and Graphical Models - Vanessa Didelez
16. Identication In Graphical Causal Models - Ilya Shpitser
17. Mediation Analysis - Johan Steen and Stijn Vansteelandt
18. Search for Causal Models - Peter Spirtes and Kun Zhang
Part V: Applications
19. Graphical Models for Forensic Analysis - A. Philip Dawid and Julia Mortera
20. Graphical Models in Molecular Systems Biology - Sach Mukherjee and Chris Oates
21. Graphical Models in Genetics, Genomics, and Metagenomics - Hongzhe Li and Jing Ma