000 aam a22 4500
999 _c211983
_d211983
008 190509b 2019 ||||| |||| 00| 0 eng d
020 _a9781498788625
082 _a519.5
_bH2
245 _aHandbook of graphical models
260 _bChapman & Hall/ CRC Press
_c2019
_aBoca Raton
300 _axviii, 536p.
_bWith index
440 _aChapman & Hall/CRC handbooks of modern statistical methods
_9379874
504 _aTable 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
520 _aA 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. https://www.crcpress.com/Handbook-of-Graphical-Models/Maathuis-Drton-Lauritzen-Wainwright/p/book/9781498788625
650 _aGraphical modeling - Statistics
_9379875
650 _aMachine learning
_9379876
650 _aMathematical statistics
_9379877
650 _aMathematical statistics - Data processing
_9379878
700 _aMaathuis, Marloes
_eEditor
_9379879
700 _aDrton, Mathias
_eEditor
_9379880
700 _aLauritzen, Steffen
_eEditor
_9379881
700 _aWainwright, Martin
_eEditor
_9379882
942 _2ddc
_cBK