Handbook of Graphical Models

Marloes Maathuis is Professor of Statistics at ETH Zurich. Mathias Drton is Professor of Statistics at the University of Copenhagen and the University of Washington. Steffen Lauritzen is Professor of Statistics at the University of Copenhagen. Martin Wainwright is Chancellor's Professor at the University of Berkeley.

Part I Conditional independencies and Markov properties 1. Conditional Independence and Basic Markov Properties - Milan Studený 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. MAP Estimation: Linear Programming Relaxation and Message-Passing Algorithms - Ofer Meshi and Alexander G. Schwing 5. Sequential Monte Carlo Methods - Arnaud Doucet and Anthony Lee
Part III Statistical inference 6. Discrete Graphical Models and their Parametrization - Luca La Rocca and Alberto Roverato 7. Gaussian Graphical Models - Caroline Uhler 8. Bayesian inference in Graphical Gaussian Models - Hélène Massam 9. Latent tree models - Piotr Zwiernik 10.Neighborhood selection methods - Po-Ling Loh 11. Nonparametric Graphical Models - Han Liu and John La□erty 12.Inference in high-dimensional graphical models - Jana Janková and Sara van de Geer
Part IV Causal inference 13. Causal Concepts and Graphical Models - Vanessa Didelez 14. Identi□cation In Graphical Causal Models - Ilya Shpitser 15. Mediation Analysis - Johan Steen and Stijn Vansteelandt 16.Search for Causal Models - Peter Spirtes and Kun Zhang
Part V Applications 17.Graphical Models for Forensic Analysis - A. Philip Dawid and Julia Mortera 18. Graphical models in molecular systems biology - Sach Mukherjee and Chris Oates 19.Graphical Models in Genetics, Genomics and Metagenomics - Hongzhe Li and Jing Ma