Handbook of graphical models - Boca Raton Chapman & Hall/ CRC Press 2019 - xviii, 536p. With index - Chapman & Hall/CRC handbooks of modern statistical methods .

Table of Contents

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 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 - Hélène Massam
11 Latent tree models - Piotr Zwiernik
12 Neighborhood selection methods - Po-Ling Loh
13 Nonparametric Graphical Models - Han Liu and John La□erty
14 Inference in high-dimensional graphical models - Jana Janková and Sara van de Geer

Part IV Causal inference
15 Causal Concepts and Graphical Models - Vanessa Didelez
16 Identi□cation 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

A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art. Key features:
* Contributions by leading researchers from a range of disciplines
* Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications
* Balanced coverage of concepts, theory, methods, examples, and applications
* Chapters can be read mostly independently, while cross-references highlight connections
The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.



Graphical modeling - Statistics
Machine learning
Mathematical statistics
Mathematical statistics - Data processing

519.5 / H2

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