Amazon cover image
Image from Amazon.com

Bayesian networks and decision graphs

By: Contributor(s): Material type: TextTextSeries: Information Science and StatisticsPublication details: New York Springer 2007Edition: 2nd edDescription: xvi, 447 pISBN:
  • 9780387682815
Subject(s): DDC classification:
  • 519.542 J3B2-2007
Summary: Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. (http://www.springer.com/gp/book/9780387682815)
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 33-A / Slot 1676 (2nd Floor, East Wing) Non-fiction General Stacks 519.542 J3B2-2007 (Browse shelf(Opens below)) Available 189337

Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis.
The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. (http://www.springer.com/gp/book/9780387682815)

There are no comments on this title.

to post a comment.