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Informationen zum Autor 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. Klappentext "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"-- Zusammenfassung Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (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. Inhaltsverzeichnis 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...
