Guy Van den Broeck

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.

Probabilistic data is motivated by the need to model uncertainty in large databases. Over the last twenty years or so, both the Database community and the Al community have studied various aspects of probabilistic relational data. Query Processing on Probabilistic Data: A Survey presents the main approaches developed in the literature, reconciling concepts developed in parallel by the two research communities. It starts with an extensive discussion of the main probabilistic data models and their relationships, followed by a brief overview of model counting and its relationship to probabilistic data. The monograph proceeds to discuss lifted probabilistic inference, a suite of techniques developed in parallel by the Database and Al communities for probabilistic query evaluation. It then provides a summary of query compilation, presenting some theoretical results highlighting limitations of various query evaluation techniques on probabilistic data. It ends with a brief discussion of some popular probabilistic data sets, systems, and applications that build on this technology.