Handbook of graphical models /
edited by Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright.
- 1 online resource.
- Chapman & Hall/CRC handbooks of modern statistical methods .
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.
9780429463976 0429463979 9780429874239 0429874235 9780429874222 0429874227 9780429874246 0429874243
Graphical modeling (Statistics)
MATHEMATICS / Applied
MATHEMATICS / Probability & Statistics / General
BUSINESS & ECONOMICS / Statistics
COMPUTERS / Machine Theory
QA279 / .H3435 2019
519.5
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.
9780429463976 0429463979 9780429874239 0429874235 9780429874222 0429874227 9780429874246 0429874243
Graphical modeling (Statistics)
MATHEMATICS / Applied
MATHEMATICS / Probability & Statistics / General
BUSINESS & ECONOMICS / Statistics
COMPUTERS / Machine Theory
QA279 / .H3435 2019
519.5