Handbook of Graphical Models

Part I Conditional independencies and Markov properties Conditional Independence and Basic Markov Properties - Milan Studeny Markov Properties for Mixed Graphical Models - Robin Evans Algebraic Aspects of Conditional Independence and Graphical Models - Thomas Kahle, Johannes Rauh, and Seth Sullivant Part II Computing with factorizing distributions Algorithms and Data Structures for Exact Computation of Marginals - Jeffrey A. Bilmes Approximate methods for calculating marginals and likelihoods - Nicholas Ruozzi MAP Estimation: Linear Programming Relaxation and Message-Passing Algorithms - Ofer Meshi and Alexander G. Schwing Sequential Monte Carlo Methods - Arnaud Doucet and Anthony Lee Part III Statistical inference Discrete Graphical Models and their Parametrization - Luca La Rocca and Alberto Roverato Gaussian Graphical Models - Caroline Uhler Bayesian inference in Graphical Gaussian Models - Helene Massam Latent tree models - Piotr Zwiernik Neighborhood selection methods - Po-Ling Loh Nonparametric Graphical Models - Han Liu and John Laerty Inference in high-dimensional graphical models - Jana Jankova and Sara van de Geer Part IV Causal inference Causal Concepts and Graphical Models - Vanessa Didelez Identication In Graphical Causal Models - Ilya Shpitser Mediation Analysis - Johan Steen and Stijn Vansteelandt Search for Causal Models - Peter Spirtes and Kun Zhang Part V Applications Graphical Models for Forensic Analysis - A. Philip Dawid and Julia Mortera Graphical models in molecular systems biology - Sach Mukherjee and Chris Oates Graphical Models in Genetics, Genomics and Metagenomics - Hongzhe Li and Jing Ma

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• Engenharia do controlo automático
• Probabilidade e Estatística