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- Covers several applications of Bayesian networks, including cardiovascular and ecological risk assessments
- Incorporates recent work on interventions and experimental data
- Describes naive Bayes models and other classifiers
- Requires only a basic understanding of algebra and calculus
- Includes a guide to software tools in an appendix
- Presents exercises at the end of each chapter
- Provides software, examples, and exercises on the book’s website

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.

**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 MODELSLearning 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.*

**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

… useful insights on Bayesian reasoning. … There are extensive examples of applications and case studies. … The exposition is clear, with many comments that help set the context for the material that is covered. The reader gets a strong sense that Bayesian networks are a work in progress.

—John H. Maindonald, *International Statistical Review* (2011), 79

**Praise for the First Edition:**

… this excellent book would also serve well for final year undergraduate courses in mathematics or statistics and is a solid first reference text for researchers wanting to implement Bayesian belief network (BBN) solutions for practical problems. … beautifully presented, nicely written, and made accessible. Mathematical ideas, some quite deep, are presented within the flow but do not get in the way. This has the advantage that students can see and interpret the mathematics in the practical context, whereas practitioners can acquire, to personal taste, the mathematical seasoning. If you are interested in applying BBN methods to real-life problems, this book is a good place to start…

—*Journal of the Royal Statistical Society, Series A*, Vol. 157(3)

Resource | OS Platform | Updated | Description | Instructions |
---|---|---|---|---|

Cross Platform | February 09, 2011 | Link to author's site | click on http://www.csse.monash.edu.au/bai/book |