Korb / Nicholson Bayesian Artificial Intelligence, Second Edition

2. Auflage 2010
ISBN: 978-1-4398-1592-2
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 491 Seiten

Reihe: Chapman & Hall/CRC Computer Science & Data Analysis

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.
New to the Second Edition

- New chapter on Bayesian network classifiers

- New section on object-oriented Bayesian networks

- New section that addresses foundational problems with causal discovery and Markov blanket discovery

- New section that covers methods of evaluating causal discovery programs

- Discussions of many common modeling errors

- New applications and case studies

- More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks

Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems.
Web Resource
The book’s website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.

Zielgruppe

Researchers and advanced undergraduate and graduate students in computer science, statistics, machine learning, data mining, mathematics, and engineering; AI practitioners and knowledge engineers.

Autoren/Hrsg.

Korb, Kevin B.

Nicholson, Ann E.

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Systemtheorie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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
A Summary, Notes, and Problems appear at the end of each chapter.

Über Autor(innen)

Kevin B. Korb is a Reader in the Clayton School of Information Technology at Monash University in Australia. He earned his Ph.D. from Indiana University. His research encompasses causal discovery, probabilistic causality, evaluation theory, informal logic and argumentation, artificial evolution, and philosophy of artificial intelligence.
Ann E. Nicholson an Associate Professor in the Clayton School of Information Technology at Monash University in Australia. She earned her Ph.D. from the University of Oxford. Her research interests include artificial intelligence, probabilistic reasoning, Bayesian networks, knowledge engineering, plan recognition, user modeling, evolutionary ethics, and data mining