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

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

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