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Bayesian Artificial Intelligence

Edition: 
2
Publisher: 
Chapman & Hall/CRC
Number of Pages: 
463
Price: 
89.95
ISBN: 
9781439815915
Date Received: 
Monday, January 10, 2011
Reviewable: 
No
Include In BLL Rating: 
No
Reviewer Email Address: 
Kevin B. Korb and Ann E. Nicholson
Series: 
Computer Science and Data Analysis Series
Publication Date: 
2011
Format: 
Hardcover
Category: 
Monograph

PROBABILISTIC REASONING
Bayesian Reasoning

Reasoning under uncertainty
Uncertainty in AI
Probability calculus
Interpretations of probability
Bayesian philosophy
The goal of Bayesian AI
Achieving Bayesian AI
Are Bayesian networks Bayesian?

Introducing Bayesian Networks
Introduction
Bayesian network basics
Reasoning with Bayesian networks
Understanding Bayesian networks
More examples

Inference in Bayesian Networks
Introduction
Exact inference in chains
Exact inference in polytrees
Inference with uncertain evidence
Exact inference in multiply-connected networks
Approximate inference with stochastic simulation
Other computations
Causal inference

Decision Networks
Introduction
Utilities
Decision network basics
Sequential decision making
Dynamic Bayesian networks
Dynamic decision networks
Object-oriented Bayesian networks

Applications of Bayesian Networks
Introduction
A brief survey of BN applications
Cardiovascular risk assessment
Goulburn Catchment Ecological Risk Assessment
Bayesian poker
Ambulation monitoring and fall detection
A Nice Argument Generator (NAG)

LEARNING CAUSAL MODELS
Learning Probabilities
Introduction
Parameterizing discrete models
Incomplete data
Learning local structure

Bayesian Network Classifiers
Introduction
Naive Bayes models
Semi-naive Bayes models
Ensemble Bayes prediction
The evaluation of classifiers

Learning Linear Causal Models
Introduction
Path models
Constraint-based learners

Learning Discrete Causal Structure
Introduction
Cooper and Herskovits’ K2
MDL causal discovery
Metric pattern discovery
CaMML: Causal discovery via MML
CaMML stochastic search
Problems with causal discovery
Evaluating causal discovery

KNOWLEDGE ENGINEERING
Knowledge Engineering with Bayesian Networks

Introduction
The KEBN process
Stage 1: BN structure
Stage 2: probability parameters
Stage 3: decision structure
Stage 4: utilities (preferences)
Modeling example: missing car
Incremental modeling
Adaptation

KEBN Case Studies
Introduction
Bayesian poker revisited
An intelligent tutoring system for decimal understanding
Goulburn Catchment Ecological Risk Assessment
Cardiovascular risk assessment

Appendix A: Notation
Appendix B: Software Packages

References

Index

Publish Book: 
Modify Date: 
Monday, January 10, 2011

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