Edition:

2

Publisher:

Chapman & Hall/CRC

Number of Pages:

463

Price:

89.95

ISBN:

9781439815915

Date Received:

Monday, January 10, 2011

Reviewable:

No

Reviewer Email Address:

Series:

Computer Science and Data Analysis Series

Publication Date:

2011

Format:

Hardcover

Audience:

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 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**

Publish Book:

Modify Date:

Monday, January 10, 2011

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