Handbook of Graphical Models: 1st Edition (e-Book) book cover

Handbook of Graphical Models

1st Edition

Edited by Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright

CRC Press

536 pages | 98 B/W Illus.

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Description

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.

Table of Contents

Part I Conditional independencies and Markov properties

Conditional Independence and Basic Markov Properties - Milan Studený

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 - Hélène Massam

Latent tree models - Piotr Zwiernik

Neighborhood selection methods - Po-Ling Loh

Nonparametric Graphical Models - Han Liu and John La□erty

Inference in high-dimensional graphical models - Jana Janková and Sara van de Geer

Part IV Causal inference

Causal Concepts and Graphical Models - Vanessa Didelez

Identi□cation 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

About the Editors

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.

About the Series

Chapman & Hall/CRC Handbooks of Modern Statistical Methods

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM037000
COMPUTERS / Machine Theory
MAT029000
MATHEMATICS / Probability & Statistics / General