Bayesian Networks and Decision Graphs: February 8, 2007

✍ Scribed by Finn V. Jensen, Thomas D. Nielsen (auth.)

• Publisher: Springer-Verlag New York
• Year: 2007
• Tongue: English
• Leaves: 457
• Series: Information Science and Statistics
• Edition: 2
• Category: Library

✦ Synopsis

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.

The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also

• provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes.
• give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge.

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• give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs.
• present a thorough introduction to state-of-the-art solution and analysis algorithms.

The book is intended as a textbook, but it can also be used for self-study and as a reference book.

Finn V. Jensen is a professor at the department of computer science at Aalborg University, Denmark.

Thomas D. Nielsen is an associate professor at the same department.

✦ Table of Contents

Front Matter....Pages i-xvi
Prerequisites on Probability Theory....Pages 1-19
Front Matter....Pages 21-21
Causal and Bayesian Networks....Pages 23-50
Building Models....Pages 51-108
Belief Updating in Bayesian Networks....Pages 109-166
Analysis Tools for Bayesian Networks....Pages 167-193
Parameter estimation....Pages 195-228
Learning the Structure of Bayesian Networks....Pages 229-264
Bayesian Networks as Classifiers....Pages 265-276
Front Matter....Pages 277-277
Graphical Languages for Specification of Decision Problems....Pages 279-342
Solution Methods for Decision Graphs....Pages 343-405
Methods for Analyzing Decision Problems....Pages 407-428
Back Matter....Pages 429-447

✦ Subjects

Probability and Statistics in Computer Science; Artificial Intelligence (incl. Robotics); Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences