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An Introduction to Lifted Probabilistic Inference
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Product Details
Author:
Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole
Series:
Neural Information Processing series
Format:
Paperback
Pages:
454
Publisher:
MIT Press (August 17, 2021)
Language:
English
ISBN-13:
9780262542593
ISBN-10:
0262542595
Weight:
29.8oz
Dimensions:
7.06" x 9.06" x 1.06"
Case Pack:
16
File:
RandomHouse-PRH_Book_Company_PRH_PRT_Onix_full_active_D20260705T122803_156890406-20260705.xml
Folder:
RandomHouse
List Price:
$70.00
As low as:
$53.90
Publisher Identifier:
P-RH
Discount Code:
A
QuickShip:
Yes
Audience:
General/trade
Country of Origin:
United States
Pub Discount:
65
Imprint:
The MIT Press
Overview
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.
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.
