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What is Bayesian Networks

Chapter 39
Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
A Bayesian Network is a model that represents a domain knowledge using a directed acyclic graph structure (to model variables and their causal relationships), and also enables these causal relationships to be quantified using probabilities. These probabilities represent the uncertainty in the domain being modeled.
Published in Chapter:
Improving Project Management of Healthcare Projects through Knowledge Elicitation
Emilia Mendes (The University of Auckland, New Zealand)
Copyright: © 2013 |Pages: 20
DOI: 10.4018/978-1-4666-3990-4.ch039
Abstract
This chapter describes a case study where Bayesian Networks (BNs) were used to construct an expert-based software effort and risk prediction model for use by a large healthcare organisation in Auckland (New Zealand) to manage healthcare software projects delivered on the Web. This model was solely elicited from expert knowledge, with the participation of seven project managers, and was validated using data from 22 past finished projects. The model led to numerous changes in process and also in business. The company adapted their existing effort and risk management process to be in line with the model that was created, and the use of a mathematically based model also led to an increase in the number of projects being outsourced to this company by other company branches worldwide. Their predictions improved significantly too. The results suggest that the use of a model that allows the representation of uncertainty, inherent in effort estimation, can outperform expert-based estimates.
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Chapter 39
Improving Project Management of Healthcare Projects through Knowledge Elicitation
A Bayesian Network is a model that represents a domain knowledge using a directed acyclic graph structure (to model variables and their causal relationships), and also enables these causal relationships to be quantified using probabilities. These probabilities represent the uncertainty in the domain being modeled.
Published in Chapter: Improving Project Management of Healthcare Projects through Knowledge Elicitation; From: Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
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Chapter 15
Bayesian Model for Evaluating Real-World Adaptation Progress of a Cyber-Physical System
A probabilistic model representing sets of variables and their dependencies.
Published in Chapter: Bayesian Model for Evaluating Real-World Adaptation Progress of a Cyber-Physical System; From: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems
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Chapter 5
Artificial Intelligence and Marketing: Progressive or Disruptive Transformation? Review of the Literature
Also known as Belief Networks or Bayesian Belief Networks are a type of probabilistic graphical model that represents relationships between variables and the uncertainty surrounding those relationships. The basic building block of a Bayesian network is a node, which represents a variable, and the edges between nodes represent the relationships or dependencies between variables. The probability distribution of each variable is modeled using a set of conditional probabilities, which describe how the variable depends on its parent variables in the network. Bayesian networks can be used to perform tasks such as probability inference, which involves computing the probability of a particular event given certain observations, and decision-making, which involves choosing the best course of action based on uncertain information. They are particularly useful in applications where the relationships between variables are complex and there is a high degree of uncertainty.
Published in Chapter: Artificial Intelligence and Marketing: Progressive or Disruptive Transformation? Review of the Literature; From: Confronting Security and Privacy Challenges in Digital Marketing
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Chapter 6
The Combination of Bayesian Networks and Stereotypes to Initialize the Learner Model in Adaptive Educational Hypermedia Systems
Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
Published in Chapter: The Combination of Bayesian Networks and Stereotypes to Initialize the Learner Model in Adaptive Educational Hypermedia Systems; From: Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities
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Chapter 392
Machine Learning
Probabilistic models for knowledge representation under uncertainty.
Published in Chapter: Machine Learning; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 5
The Determination of Learning Styles in a Learner Model Using the Combination of Bayesian Network and the Overlay Model
Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
Published in Chapter: The Determination of Learning Styles in a Learner Model Using the Combination of Bayesian Network and the Overlay Model; From: Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities
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Chapter 3
Bayesian Networks for Modeling and Inferring Gene Regulatory Networks
A Bayesian network is a probabilistic graphical model. It contains of a graph whose vertices represent variables, for instance random variables. The directed edges of the graph encode direct dependency relation of one variable to another. Bayesian networks can be used to predict the state of variables, when other variables are fixed. In addition, Bayesian networks can be learned from sampled data.
Published in Chapter: Bayesian Networks for Modeling and Inferring Gene Regulatory Networks; From: Handbook of Research on Computational Methodologies in Gene Regulatory Networks
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Chapter 25
How CyberCoaching System Works
A statistical probabilistic approach to reasoning derived from evidence.
Published in Chapter: How CyberCoaching System Works; From: Handbook of Research on Instructional Systems and Technology
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Chapter 8
Combining the Overlay Model and Bayesian Networks to Determine Learning Styles in AHES
Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
Published in Chapter: Combining the Overlay Model and Bayesian Networks to Determine Learning Styles in AHES; From: Handbook of Research on E-Assessment in Higher Education
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Chapter 5
Managing the Learner Model With Multi-Entity Bayesian Networks in Adaptive Hypermedia Systems
Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
Published in Chapter: Managing the Learner Model With Multi-Entity Bayesian Networks in Adaptive Hypermedia Systems; From: Cognitive Computing in Technology-Enhanced Learning
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Chapter 7
A Learner Model Based on Multi-Entity Bayesian Networks in Adaptive Hypermedia Educational Systems
Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
Published in Chapter: A Learner Model Based on Multi-Entity Bayesian Networks in Adaptive Hypermedia Educational Systems; From: Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities
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Chapter 4
A Learner Model Based on Bayesian Networks in Adaptive Educational Hypermedia Systems
Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
Published in Chapter: A Learner Model Based on Bayesian Networks in Adaptive Educational Hypermedia Systems; From: Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities
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Chapter 14
Artificial Intelligence a Driver for Digital Transformation
Is a graph-based model representing a set of variables and their dependencies, focused on decision-making processes.
Published in Chapter: Artificial Intelligence a Driver for Digital Transformation; From: Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy
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Chapter 25
Web 2.0 Effort Estimation
A technique that enables the construction of a model that supports reasoning with uncertainty due to the way in which it incorporates existing knowledge of a complex domain (Pearl, 1988). This knowledge is represented using two parts. The first, the qualitative part, represents the structure of a BN as depicted by a directed acyclic graph (digraph). The digraph’s nodes represent the relevant variables (factors) in the domain being modelled, which can be of different types (e.g. observable or latent, categorical). The digraph’s arcs represent the causal relationships between variables, where relationships are quantified probabilistically (Woodberry et al., 2004). The second, the quantitative part, associates a node probability table (NPT) to each node, its probability distribution. A parent node’s NPT describes the relative probability of each state (value)
Published in Chapter: Web 2.0 Effort Estimation; From: Handbook of Research on Web 2.0, 3.0, and X.0: Technologies, Business, and Social Applications
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Chapter 3
Development of Bayesian Networks From Use Case Diagrams for Managing the Learner Model
Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
Published in Chapter: Development of Bayesian Networks From Use Case Diagrams for Managing the Learner Model; From: Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities
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