A Bayesian Belief Network Approach for Modeling Complex Domains

A Bayesian Belief Network Approach for Modeling Complex Domains

Ben K. Daniel (University of Saskatchewan, Canada), Juan-Diego Zapata-Rivera (Educational Testing Service, USA) and Gordon I. McCalla (University of Saskatchewan, Canada)
Copyright: © 2007 |Pages: 29
DOI: 10.4018/978-1-59904-141-4.ch002
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Bayesian belief networks (BBNs) are increasingly used for understanding and simulating computational models in many domains. Though BBN techniques are elegant ways of capturing uncertainties, knowledge engineering effort required to create and initialize the network has prevented many researchers from using them. Even though the structure of the network and its conditional & initial probabilities could be learned from data, data is not always available or it is too costly to obtain. In addition, current algorithms that can be used to learn relationships among variables, initial and conditional probabilities from data are often complex and cumbersome to employ. Qualitative-based approaches applied to the creation of graphical models can be used to create initial computational models that can help researchers analyze complex problems and provide guidance and support for decision-making. Initial BBN models can be refined once appropriate data is obtained. This chapter extends the use of BBNs to help experts make sense of complex social systems (e.g., social capital in virtual learning communities) using a Bayesian model as an interactive simulation tool. Scenarios are used to find out whether the model is consistent with the expert’s beliefs. The sensitivity analysis was conducted to help explain how the model reacted to different sets of evidence. Currently, we are in the process of refining the initial probability values presented in the model using empirical data and developing more authentic scenarios to further validate the model.

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Table of Contents
K. R. Rao
Chapter 1
Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, Zhangbing Zhou
Naive Bayesian network (NB) is a simple yet powerful Bayesian network. Even with a strong independency assumption among the features, it... Sample PDF
A Novel Discriminative Naive Bayesian Network for Classification
Chapter 2
Ben K. Daniel, Juan-Diego Zapata-Rivera, Gordon I. McCalla
Bayesian belief networks (BBNs) are increasingly used for understanding and simulating computational models in many domains. Though BBN techniques... Sample PDF
A Bayesian Belief Network Approach for Modeling Complex Domains
Chapter 3
Sachin Shetty, Min Song, Mansoor Alam
A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic... Sample PDF
Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach
Chapter 4
Dimitris Margaritis, Christos Faloutsos, Sebastian Thrun
We present a novel method for answering count queries from a large database approximately and quickly. Our method implements an approximate DataCube... Sample PDF
NetCube: Fast, Approximate Database Queries Using Bayesian Networks
Chapter 5
Helge Langseth, Luigi Portinale
Over the last decade, Bayesian networks (BNs) have become a popular tool for modeling many kinds of statistical problems. In this chapter we will... Sample PDF
Applications of Bayesian Networks in Reliability Analysis
Chapter 6
Sumeet Gupta, Hee-Wong Kim
This chapter deals with the application of Bayesian modeling as a management decision support tool for management information systems (MIS)... Sample PDF
Application of Bayesian Modeling to Management Information Systems: A Latent Scores Approach
Chapter 7
Andreas Savaki, Jiebo Luo, Michael Kane
Image understanding deals with extracting and interpreting scene content for use in various applications. In this chapter, we illustrate that... Sample PDF
Bayesian Networks for Image Understanding
Chapter 8
Pedro M. Jorge, Arnaldo J. Abrantes, João M. Lemos, Jorge S. Marques
This chapter describes an algorithm for tracking groups of pedestrians in video sequences. The main difficulties addressed in this work concern... Sample PDF
Long Term Tracking of Pedestrians with Groups and Occlusions
Chapter 9
Qian Diao, Jianye Lu, Wei Hu, Yimin Zhang, Gary Bradski
In a visual tracking task, the object may exhibit rich dynamic behavior in complex environments that can corrupt target observations via background... Sample PDF
DBN Models for Visual Tracking and Prediction
Chapter 10
David Lo
In applications where the locations of human subjects are needed, for example, human-computer interface, video conferencing, and security... Sample PDF
Multimodal Human Localization Using Bayesian Network Sensor Fusion
Chapter 11
C. Notarnicola
This chapter introduces the use of Bayesian methodology for inversion purposes: the extraction of bio-geophysical parameters from remotely sensed... Sample PDF
Retrieval of Bio-Geophysical Parameters from Remotely Sensing Data by Using Bayesian Methodology
Chapter 12
Arunkumar Chinnasamy, Sudhanshu Patwardhan, Wing-Kin Sung
The end of the 20th century and the advent of the new millennium have brought in a true merger of sciences for the benefit of mankind. The biggest... Sample PDF
Application of Bayesian Network in Drug Discovery and Development Process
Chapter 13
Seiya Imoto, Satoru Miyano
In cells, genes interact with each other and this system can be viewed as directed graphs. A gene network is a graphical representation of... Sample PDF
Bayesian Network Approach to Estimate Gene Networks
Chapter 14
Vipin Narang, Rajesh Chowdhary, Ankush Mittal, Wing-Kin Sung
A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to... Sample PDF
Bayesian Network Modeling of Transcription Factor Binding Sites: A Tutorial
Chapter 15
Tie-Fei Liu, Wing-Kin Sung, Ankush Mittal
Exact determination of a gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Currently... Sample PDF
Application of Bayesian Network in Learning Gene Network
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