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Handbook of Graphical Models

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

Print publication date:  November  2018
Online publication date:  November  2018

Print ISBN: 9781498788625
eBook ISBN: 9780429463976
Adobe ISBN:

10.1201/9780429463976
 Cite  Marc Record

Book description

<P>A graphical model is a statistical model that is&nbsp;represented by&nbsp;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.</P> <P></P> <P>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.</P> <P></P> <P>Key features:</P> <P>* Contributions by leading researchers from a range of disciplines</P> <P>* Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications</P> <P>* Balanced coverage of concepts, theory, methods, examples, and applications</P> <P>* Chapters can be read mostly independently, while cross-references highlight connections</P> <P></P> <P>The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.</P> <P></P>

Table of contents

Prelims Download PDF
Chapter  1:  Conditional Independence and Basic Markov Properties Download PDF
Chapter  2:  Markov Properties for Mixed Graphical Models Download PDF
Chapter  3:  Algebraic Aspects of Conditional Independence and Graphical Models Download PDF
Chapter  4:  Algorithms and Data Structures for Exact Computation of Marginals Download PDF
Chapter  5:  Approximate Methods for Calculating Marginals and Likelihoods Download PDF
Chapter  6:  MAP Estimation: Linear Programming Relaxation and Message-Passing Algorithms Download PDF
Chapter  7:  Sequential Monte Carlo Methods Download PDF
Chapter  8:  Discrete Graphical Models and Their Parameterization Download PDF
Chapter  9:  Gaussian Graphical Models Download PDF
Chapter  10:  Bayesian Inference in Graphical Gaussian Models Download PDF
Chapter  11:  Latent Tree Models Download PDF
Chapter  12:  Neighborhood Selection Methods Download PDF
Chapter  13:  Nonparametric Graphical Models Download PDF
Chapter  14:  Inference in High-Dimensional Graphical Models Download PDF
Chapter  15:  Causal Concepts and Graphical Models Download PDF
Chapter  16:  Identification in Graphical Causal Models Download PDF
Chapter  17:  Mediation Analysis Download PDF
Chapter  18:  Search for Causal Models Download PDF
Chapter  19:  Neighborhood Selection Methods Download PDF
Chapter  20:  Graphical Models in Molecular Systems Biology Download PDF
Chapter  21:  Graphical Models in Genetics, Genomics, and Metagenomics Download PDF
Index Download PDF
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