Probabilistic graphical models : principles and techniques

By:

Koller, Daphne

Contributor(s):

Friedman, Nir

Material type: Text

TextSeries: Adaptive computation and machine learningPublication details: United States of America MIT Press 2009Description: xxxv, 1233 pISBN:

9780262013192 (hb.)

Subject(s):

DDC classification:

519.5420285 KOL

Summary: Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Plaksha University Library	Mathematics	519.5420285 KOL (Browse shelf(Opens below))	Available		003569
Book	Plaksha University Library	Mathematics	519.5420285 KOL (Browse shelf(Opens below))	Available		003570

Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.

There are no comments on this title.

to post a comment.