Home : Book details

An Introduction to Lifted Probabilistic Inference (Neural Information Processing series) free ebook download

Category:
Computer Science

Views: 256
Likes: 38

Catalogue

Author(s):	Guy Van den Broeck

Date:	2021	Format:	pdf	Language:	English	ISBN/ASIN:	0262542595
Pages:	454	OCR:		Quality:		ISBN13:
Uploader:	risotor	Upload Date:	2/21/2022 9:37:34 PM

To download click on link in the Links Table below

Description: Click to see full description

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.
Read more

Links Table

	Title	Link	Password	Size (MB)	Thank to	Inform Admin
3093006	Book	https://florenfile.com/9107xhgph28b/0262...		0.00	risotor

Tags: Inference Information Introduction Lifted Neural Probabilistic Processing

Title	Link	Password	Size	Reason

Those who downloaded this book also downloaded the following books:
Comments

New comment:

An Introduction to Lifted Probabilistic Inference (Neural Information Processing series) free ebook download

Category: Computer Science

Description: Click to see full description

Category:
Computer Science