Chapter: Discrete Graphical Models and Their Parameterization

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Home > Handbook of Graphical Models > Discrete Graphical Models and Their Parameterization

Handbook of Graphical Models |

Marloes Maathuis; Mathias Drton; Steffen Lauritzen; Martin Wainwright

Routledge

Discrete Graphical Models and Their Parameterization

Chapter Info

Authors

Luca La Rocca; Alberto Roverato

ARI Id

1665833476936_13886982

Access

Not Available Free

Pages

191-216

DOI

10.1201/9780429463976-8

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Chapters/Headings	Author(s)	Pages	Info
Part I Conditional independencies and Markov properties		1-2
Conditional Independence and Basic Markov Properties	Milan Studený	3-38
Markov Properties for Mixed Graphical Models	Robin Evans	39-60
Algebraic Aspects of Conditional Independence and Graphical Models	Thomas Kahle; Johannes Rauh; Seth Sullivant	61-80
Part II Computing with factorizing distributions		81-82
Algorithms and Data Structures for Exact Computation of Marginals	Jeffrey A. Bilmes	83-116
Approximate Methods for Calculating Marginals and Likelihoods	Nicholas Ruozzi	117-140
MAP Estimation: Linear Programming Relaxation and Message-Passing Algorithms	Ofer Meshi; Alexander G. Schwing	141-164
Sequential Monte Carlo Methods	Arnaud Doucet; Anthony Lee	165-188
Part III Statistical inference		189-190
Discrete Graphical Models and Their Parameterization	Luca La Rocca; Alberto Roverato	191-216
Gaussian Graphical Models	Caroline Uhler	217-238
Bayesian Inference in Graphical Gaussian Models	Hélène Massam	239-264
Latent Tree Models	Piotr Zwiernik	265-288
Neighborhood Selection Methods	Po-Ling Loh	289-308
Nonparametric Graphical Models	Han Liu; John Lafferty	309-324
Inference in High-Dimensional Graphical Models	Jana Janková; Sara van de Geer	325-350
Part IV Causal inference		351-352
Causal Concepts and Graphical Models	Vanessa Didelez	353-380
Identification in Graphical Causal Models	Ilya Shpitser	381-404
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