書誌事項

Handbook of graphical models

edited by Marloes Maathuis ... [et al.]

(Handbooks of modern statistical methods / Series editors, Garrett Fitzmaurice)

CRC Press, 2020, c2019

  • : pbk

この図書・雑誌をさがす
注記

Other editors: Mathias Drton, Steffen Lauritzen, Martin Wainwright

Includes bibliographical references and index

"A Chapman & Hall book"

内容説明・目次

内容説明

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. 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.

目次

Part I: Conditional independencies and Markov properties. 1. Conditional Independence and Basic Markov Properties - Milan Studeny 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 - Helene Massam 11. Latent Tree Models - Piotr Zwiernik 12.Neighborhood Selection Methods - Po-Ling Loh 12. Nonparametric Graphical Models - Han Liu and John Lafferty 14. Inference in High-Dimensional Graphical Models - Jana Jankova and Sara van de Geer Part IV: Causal inference 15. Causal Concepts and Graphical Models - Vanessa Didelez 16. Identication 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

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詳細情報
  • NII書誌ID(NCID)
    BC04930716
  • ISBN
    • 9780367732608
  • LCCN
    2018010969
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Boca Raton
  • ページ数/冊数
    xviii, 536 p.
  • 大きさ
    26 cm
  • 分類
  • 件名
  • 親書誌ID
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