Bayesian Network Technologies: Applications and Graphical Models, 1st Edition

  • Published By:
  • ISBN-10: 159904143X
  • ISBN-13: 9781599041438
  • DDC: 519.542
  • Grade Level Range: College Freshman - College Senior
  • 356 Pages | eBook
  • Original Copyright 2007 | Published/Released September 2007
  • This publication's content originally published in print form: 2007

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About

Overview

Bayesian networks are now being used in a variety of artificial intelligence applications. These networks are high-level representations of probability distributions over a set of variables that are used for building a model of the problem domain.

Bayesian Network Technologies: Applications and Graphical Models provides an excellent and well-balanced collection of areas where Bayesian networks have been successfully applied. This book describes the underlying concepts of Bayesian Networks in an interesting manner with the help of diverse applications, and theories that prove Bayesian networks valid. Bayesian Network Technologies provides specific examples of how Bayesian networks are powerful machine learning tools critical in solving real-life problems.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Bayesian Network Technologies: Applications and Graphical Models: Table of Contents.
Foreword.
Preface.
1: Modeling and Classification Using Bayesian Networks.
2: A Novel Discriminative Naive Bayesian Network for Classification.
3: A Bayesian Belief Network Approach for Modeling Complex Domains.
4: Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach.
5: NetCube: Fast, Approximate Database Queries Using Bayesian Networks.
6: Applications of Bayesian Networks in Reliability Analysis.
7: Application of Bayesian Modeling to Management Information Systems: A Latent Scores Approach.
8: Bayesian Network for Image Processing and Related Applications.
9: Bayesian Networks for Image Understanding.
10: Long Term Tracking of Pedestrians with Groups and Occlusions.
11: DBN Models for Visual Tracking and Prediction.
12: Multimodal Human Localization Using Bayesian Network Sensor Fusion.
13: Retrieval of Bio-Geophysical Parameters from Remotely Sensing Data by Using Bayesian Methodology.
14: Bayesian Networks for Bioinformatics Applications.
15: Application of Bayesian Network in Drug Discovery and Development Process.
16: Bayesian Network Approach to Estimate Gene Networks.
17: Bayesian Network Modeling of Transcription Factor Binding Sites: A Tutorial.
18: Application of Bayesian Network in Learning Gene Network.
About the Authors.
Index.