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Springer - Bayesian Networks and Decision Graphs Book

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Description

Explore the power of probabilistic graphical models and decision graphs with this academic text from Springer. This book serves as a guide for reasoning and decision making under uncertainty, offering a natural way to specify problem domains. From a computational standpoint, these modeling languages support efficient algorithms for automatic construction and various query answering tasks. Readers can learn to implement belief updating and find the most probable explanation for observed evidence. The text also covers methods for detecting conflicts in evidence, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. It is an essential resource for those studying information science and statistics.

Key Features

This Springer publication covers probabilistic graphical models and decision graphs for reasoning under uncertainty.

Learn efficient algorithms for automatic construction and query answering within complex problem domains.

The text provides methods for belief updating and finding the most probable explanation for observed evidence.

Gain technical knowledge on detecting evidence conflicts and determining optimal strategies.

Study advanced techniques for relevance analysis and performing sensitivity analysis within networks.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
06 June 2007
Listed Since
16 February 2007

Barcode

No barcode data available

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