An Introduction to Lifted Probabilistic Inference

Guy van den Broeck

ISBN 10: 0262542595 / ISBN 13: 9780262542593

Edité par MIT Press, 2021

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9780262542593

Détails bibliographiques

• Titre: An Introduction to Lifted Probabilistic Inference
• Éditeur: MIT Press
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 0262542595
• ISBN à 13 chiffres: 9780262542593
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 229 L x 178 H x 29 P (en millimètres) 229 millimètres de largeur par 178 millimètres de hauteur par 29 millimètres de profondeur
• Poids: 1135 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

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.