Bayesian Networks and Decision Graphs
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 algor...
| Main Authors: | , |
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| Corporate Author: | |
| Language: | English |
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New York, NY :
Springer New York : Imprint: Springer,
2007.
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| Edition: | 2nd ed. 2007. |
| Series: | Information Science and Statistics,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-0-387-68282-2 |
Table of Contents:
- Prerequisites on Probability Theory
- Prerequisites on Probability Theory
- Probabilistic Graphical Models
- Causal and Bayesian Networks
- Building Models
- Belief Updating in Bayesian Networks
- Analysis Tools for Bayesian Networks
- Parameter estimation
- Learning the Structure of Bayesian Networks
- Bayesian Networks as Classifiers
- Decision Graphs
- Graphical Languages for Specification of Decision Problems
- Solution Methods for Decision Graphs
- Methods for Analyzing Decision Problems.