Introduction to Lifted Probabilistic Inference
Published on 17. August 2021
Book
Paperback/Softback
454 pages
978-0-262-54259-3 (ISBN)
Description
"The book presents an introduction to, and an authoritative guide, for anyone interested in the problem of probabilistic inference in the presence of symmetries/structured models"--
More details
Series
Language
English
Place of publication
Cambridge (Massachusetts)
United States
Publishing group
MIT Press Ltd
Dimensions
Height: 229 mm
Width: 178 mm
Thickness: 29 mm
Weight
1135 gr
ISBN-13
978-0-262-54259-3 (9780262542593)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Guy Van den Broeck | Kristian Kersting | Sriraam Natarajan
An Introduction to Lifted Probabilistic Inference
E-Book
08/2021
MIT Press
€68.49
Available for download
Persons
Guy Van den Broeck is Associate Professor of Computer Science at the University of California, Los Angeles. Kristian Kersting is Professor in the Computer Science Department and the Centre for Cognitive Science at Technische Universität Darmstadt. Sriraam Natarajan is Professor and the Director of the Center for Machine Learning in the Department of Computer Science at University of Texas at Dallas. David Poole is Professor in the Department of Computer Science at the University of British Columbia.
Content
List of Figures
Contributors
Preface
I OVERVIEW
1 Statistical Relational AI: Representation, Inference and Learning
2 Modeling and Reasoning with Statistical Relational Representation
3 Statistical Relational Learning
II EXACT INFERENCE
4 Lifted Variable Elimination
5 Search-Based Exact Lifted Inference
6 Lifted Aggregation and Skolemization for Directed Models
7 First-Order Knowledge Compilation
8 Domain Liftability
9 Tractability through Exchangeability: The Statistics of Lifting
III APPROXIMATE INFERENCE
10 Lifted Markov Chain Monte Carlo
11 Lifted Message Passing for Probabilistic and Combinatorial Problems
12 Lifted Generalized Belief Propagation: Relax, Compensate and Recover
13 Liftability Theory of Variational Inference
14 Lifted Inference for Hybrid Relational Models
IV BEYOND PROBABILISTIC INFERENCE
15 Color Refinement and Its Applications
16 Stochastic Planning and Lifted Inference
Bibliography
Index
Contributors
Preface
I OVERVIEW
1 Statistical Relational AI: Representation, Inference and Learning
2 Modeling and Reasoning with Statistical Relational Representation
3 Statistical Relational Learning
II EXACT INFERENCE
4 Lifted Variable Elimination
5 Search-Based Exact Lifted Inference
6 Lifted Aggregation and Skolemization for Directed Models
7 First-Order Knowledge Compilation
8 Domain Liftability
9 Tractability through Exchangeability: The Statistics of Lifting
III APPROXIMATE INFERENCE
10 Lifted Markov Chain Monte Carlo
11 Lifted Message Passing for Probabilistic and Combinatorial Problems
12 Lifted Generalized Belief Propagation: Relax, Compensate and Recover
13 Liftability Theory of Variational Inference
14 Lifted Inference for Hybrid Relational Models
IV BEYOND PROBABILISTIC INFERENCE
15 Color Refinement and Its Applications
16 Stochastic Planning and Lifted Inference
Bibliography
Index