Bayesian Network Technologies: Applications and Graphical Models: Applications and Graphical Models

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Mittal, Ankush
Idea Group Inc (IGI), Mar 31, 2007 - Computers - 368 pages
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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: Applications and Graphical Models provides specific examples of how Bayesian networks are powerful machine learning tools critical in solving real-life problems.

 

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Contents

A Bayesian Belief Network Approach for Modeling Complex Domains
3
Data Mining of Bayesian Network Structure Using a Semantic Genetic AlgorithmBased Approach
32
NetCube Fast Approximate Database Queries Using Bayesian Networks
44
Applications of Bayesian Networks in Reliability Analysis
84
Application of Bayesian Modeling to Management Information Systems A Latent Scores Approach
103
Bayesian Network for Image Processing and Related Applications
127
Bayesian Networks for Image Understanding
128
Long Term Tracking of Pedestrians with Groups and Occlusions
151
Retrieval of BioGeophysical Parameters from Remotely Sensing Data by Using Bayesian Methodology
222
Bayesian Networks for Bioinformatics Applications
253
Application of Bayesian Network in Drug Discovery and Development Process
254
Bayesian Network Approach to Estimate Gene Networks
269
Bayesian Network Modeling of Transcription Factor Binding Sites A Tutorial
300
Application of Bayesian Network in Learning Gene Network
319
About the Authors
342
Index
352

DBN Models for Visual Tracking and Prediction
176
Multimodal Human Localization Using Bayesian Network Sensor Fusion
194

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About the author (2007)

Ankush Mittal received the B. Tech. (Computer Science and Engg.) and M. S. by Research (Computer Science and Engg.) degrees from the Indian Institute of Technology, Delhi in 1996 and 1998 respectively. He got his PhD degree from Electrical and Computer Engg, The National University of Singapore. From March 2001 for around two years, he was a faculty member in the Department of Computer Science, National University of Singapore. He is presently serving as Assistant Professor at Indian Institute of Technology, Roorkee. His research interests include image processing, bioinformatics and E-learning. He has published more than 90 papers in top journals and conferences.

Ashraf A. Kassim is with the Electrical & Computer Engineering Department of the National University of Singapore (NUS) and vice-dean of the NUS School of Engineering. He obtained his Bachelor of Engineering with first class honors and Master of Engineering in electrical engineering from NUS, before receiving his PhD from Carnegie Mellon University (1993). Prior to joining NUS, Dr. Kassim was involved in machine vision research at Texas Instruments. His main research interests are in the areas of computer vision, image and video processing. He has over 100 international journal and conference publications. He has been a program and organizing committee member of a number of international conferences. Dr. Kassim is an editor of Machine Vision and Applications Journal. [Editor]

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