Handbook Of Graphical Models

Author : Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright
Publisher : CRC Press
Page : 536 pages
File Size : 47,9 Mb
Release : 2018-11-12
Category : Mathematics
ISBN : 9780429874246

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Handbook of Graphical Models by Marloes Maathuis,Mathias Drton,Steffen Lauritzen,Martin Wainwright Pdf

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.

Author : Mathias Drton,Steffen Lilholt Lauritzen,Marloes Maathuis,Martin Wainwright
Publisher : Unknown
Page : 128 pages
File Size : 55,7 Mb
Release : 2018
Category : Electronic books
ISBN : 1498788637

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Handbook of Graphical Models by Mathias Drton,Steffen Lilholt Lauritzen,Marloes Maathuis,Martin Wainwright Pdf

"Graphical models are a statistical tool used for a wide range of applications. There has been a huge amount of research in this topic across statistics, mathematics and computer science in the last few decades, and the timing is right for a handbook that presents an overview of the state-of-the-art. This handbook presents a comprehensive overview of the area through a collection of 25-30 chapters from some of the leading researchers. Each chapter has been carefully edited to ensure that the handbook is consistent in style, level and notation, and that it is accessible for graduate students and researchers new to the topic. It is sure to become a landmark reference in the area."--Provided by publisher.

Author : Steffen L. Lauritzen
Publisher : Clarendon Press
Page : 314 pages
File Size : 51,7 Mb
Release : 1996-05-02
Category : Mathematics
ISBN : 9780191591228

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Graphical Models by Steffen L. Lauritzen Pdf

The idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables. The use of graphical models in statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides the first comprehensive and authoritative account of the theory of graphical models and is written by a leading expert in the field. It contains the fundamental graph theory required and a thorough study of Markov properties associated with various type of graphs. The statistical theory of log-linear and graphical models for contingency tables, covariance selection models, and graphical models with mixed discrete-continous variables in developed detail. Special topics, such as the application of graphical models to probabilistic expert systems, are described briefly, and appendices give details of the multivarate normal distribution and of the theory of regular exponential families. The author has recently been awarded the RSS Guy Medal in Silver 1996 for his innovative contributions to statistical theory and practice, and especially for his work on graphical models.

Author : Stephen L. Morgan
Publisher : Springer Science & Business Media
Page : 423 pages
File Size : 46,9 Mb
Release : 2013-04-22
Category : Social Science
ISBN : 9789400760943

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Handbook of Causal Analysis for Social Research by Stephen L. Morgan Pdf

What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.

Author : Warren J. Hehre,W. Wayne Huang,Janet E. Nelson
Publisher : Unknown
Page : 99 pages
File Size : 43,7 Mb
Release : 1997-06-01
Category : Electronic
ISBN : 1890661015

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A Guide to Graphical Models and Graphical Modeling in Spartan by Warren J. Hehre,W. Wayne Huang,Janet E. Nelson Pdf

Author : Jonathan L. Gross,Jay Yellen,Ping Zhang
Publisher : CRC Press
Page : 1634 pages
File Size : 54,7 Mb
Release : 2013-12-17
Category : Mathematics
ISBN : 9781439880180

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Handbook of Graph Theory, Second Edition by Jonathan L. Gross,Jay Yellen,Ping Zhang Pdf

In the ten years since the publication of the best-selling first edition, more than 1,000 graph theory papers have been published each year. Reflecting these advances, Handbook of Graph Theory, Second Edition provides comprehensive coverage of the main topics in pure and applied graph theory. This second edition—over 400 pages longer than its predecessor—incorporates 14 new sections. Each chapter includes lists of essential definitions and facts, accompanied by examples, tables, remarks, and, in some cases, conjectures and open problems. A bibliography at the end of each chapter provides an extensive guide to the research literature and pointers to monographs. In addition, a glossary is included in each chapter as well as at the end of each section. This edition also contains notes regarding terminology and notation. With 34 new contributors, this handbook is the most comprehensive single-source guide to graph theory. It emphasizes quick accessibility to topics for non-experts and enables easy cross-referencing among chapters.

Author : Anonim
Publisher : Elsevier
Page : 458 pages
File Size : 45,8 Mb
Release : 2011-08-11
Category : Mathematics
ISBN : 9780080471266

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Handbook of Latent Variable and Related Models by Anonim Pdf

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine, public health and sociology. - Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

Author : Mahlet G. Tadesse,Marina Vannucci
Publisher : CRC Press
Page : 762 pages
File Size : 55,7 Mb
Release : 2021-12-24
Category : Mathematics
ISBN : 9781000510256

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Handbook of Bayesian Variable Selection by Mahlet G. Tadesse,Marina Vannucci Pdf

Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, partitioning methods, as well as variable selection in decision trees and edge selection in graphical models. The handbook targets graduate students and established researchers who seek to understand the latest developments in the field. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions. Features: Provides a comprehensive review of methods and applications of Bayesian variable selection. Divided into four parts: Spike-and-Slab Priors; Continuous Shrinkage Priors; Extensions to various Modeling; Other Approaches to Bayesian Variable Selection. Covers theoretical and methodological aspects, as well as worked out examples with R code provided in the online supplement. Includes contributions by experts in the field. Supported by a website with code, data, and other supplementary material

Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 50,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885

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Graph Representation Learning by William L. William L. Hamilton Pdf

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Author : Henning Best,Christof Wolf
Publisher : SAGE
Page : 577 pages
File Size : 49,8 Mb
Release : 2013-12-20
Category : Social Science
ISBN : 9781473914384

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The SAGE Handbook of Regression Analysis and Causal Inference by Henning Best,Christof Wolf Pdf

′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Author : Michael D. Lee,Eric-Jan Wagenmakers
Publisher : Cambridge University Press
Page : 279 pages
File Size : 49,7 Mb
Release : 2014-04-03
Category : Psychology
ISBN : 9781107653917

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Bayesian Cognitive Modeling by Michael D. Lee,Eric-Jan Wagenmakers Pdf

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Author : Mittal, Ankush,Kassim, Ashraf
Publisher : IGI Global
Page : 368 pages
File Size : 53,6 Mb
Release : 2007-03-31
Category : Computers
ISBN : 9781599041438

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Bayesian Network Technologies: Applications and Graphical Models by Mittal, Ankush,Kassim, Ashraf Pdf

"This book provides an excellent, well-balanced collection of areas where Bayesian networks have been successfully applied; it describes the underlying concepts of Bayesian Networks with the help of diverse applications, and theories that prove Bayesian networks valid"--Provided by publisher.

Author : Michael Waldmann
Publisher : Oxford University Press
Page : 769 pages
File Size : 55,9 Mb
Release : 2017
Category : Psychology
ISBN : 9780199399550

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The Oxford Handbook of Causal Reasoning by Michael Waldmann Pdf

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields.

Author : Michael Stumpf,David J. Balding,Mark Girolami
Publisher : John Wiley & Sons
Page : 624 pages
File Size : 51,9 Mb
Release : 2011-09-09
Category : Science
ISBN : 9781119952046

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Handbook of Statistical Systems Biology by Michael Stumpf,David J. Balding,Mark Girolami Pdf

Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.

Author : Judea Pearl,Madelyn Glymour,Nicholas P. Jewell
Publisher : John Wiley & Sons
Page : 162 pages
File Size : 45,8 Mb
Release : 2016-01-25
Category : Mathematics
ISBN : 9781119186861

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Causal Inference in Statistics by Judea Pearl,Madelyn Glymour,Nicholas P. Jewell Pdf

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.