 Название: An Introduction to Lifted Probabilistic Inference
Автор: Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan
Издательство: The MIT Press
Серия: Neural Information Processing
Год: 2021
Страниц: 630
Язык: английский
Формат: epub
Размер: 15.9 MB
Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models.
Statistical relational Artificial Intelligence (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field.
After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.
Contents:
• List of Figures
• Contributors
• Preface
• I OVERVIEW
• 1 Statistical Relational AI: Representation, Inference and Learning
• 2 Modeling and Reasoning with Statistical Relational Representations
• 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
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An Introduction to Lifted Probabilistic Inference 
