Bayesian Network

Ahmed Rebai

• Publisher: IntechOpen
• Year: 2010
• Print ISBN: 9789533071244
• eBook ISBN: 9789535149033
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic construction of expert systems in several different contexts. The range of applications of Bayesian networks currently extends over almost all fields including engineering, biology and medicine, information and communication technologies and finance. This book is a collection of original contributions to the methodology and applications of Bayesian networks. It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems. Readers that are not familiar with this tool, but have some technical background, will find in this book all necessary theoretical and practical information on how to use and implement Bayesian networks in their own work. There is no doubt that this book constitutes a valuable resource for engineers, researchers, students and all those who are interested in discovering and experiencing the potential of this major tool of the century.

Table of contents

• Bayesian Network
• Contents
• Preface
• Chapter 1 Learning parameters and structure of Bayesian networks using an Implicit framework
• Chapter 2 Design of evolutionary methods applied to the learning of Bayesian network structures
• Chapter 3 Probabilistic inferences in Bayesian networks
• Chapter 4 Advanced algorithms of bayesian network learning and inference from inconsistent prior knowledge and sparse data with applications in computational biology and computer vision
• Chapter 5 Group decision making using Bayesian network inference with qualitative expert knowledge
• Chapter 6 Forming object concept using Bayesian network
• Chapter 7 Guiding complex design optimisation using Bayesian Networks
• Chapter 8 Method of multi-source testing information fusion based on bayesian networks
• Chapter 9 Dynamic data feed to bayesian network model and smile web application
• Chapter 10 Markovian approach to time transition inference on bayesian networks
• Chapter 11 miniTUBA: a web-based dynamic Bayesian network analysis system and an application for host-pathogen interaction analysis
• Chapter 12 Joining Analytic Network Process and Bayesian Network model for fault spreading problem
• Chapter 13 Monitoring of complex processes with Bayesian networks
• Chapter 14 Bayesian Networks for Network Intrusion Detection
• Chapter 15 A novel probabilistic approach for analysis and planning of large capillarity broadband networks based on ADSL2+ technology
• Chapter 16 Optimization strategies for improving the interpretability of bayesian networks: an application in power systems
• Chapter 17 Strategy for Wireless Local Area Networks Planning and Performance Evaluation through Bayesian Networks as Computational Intelligence Approach
• Chapter 18 Causal modelling based on bayesian networks for preliminary design of buildings
• Chapter 19 Bayesian networks methods for traffic flow prediction
• Chapter 20 Accommodating uncertainty in grazing land condition assessment using Bayesian Belief Networks
• Chapter 21 Classification of categorical and numerical data on selected subset of features
• Chapter 22 Learning self-similarities for action recognition using conditional random fields
• Chapter 23 Probabilistic modelling and recursive bayesian estimation of trust in wireless sensor networks
• Chapter 24 Time-frequency analysis using Bayesian regularized neural network model