Search the World's Largest Database of Information Science & Technology Terms & Definitions
InfInfoScipedia LogoScipedia
A Free Service of IGI Global Publishing House
Below please find a list of definitions for the term that
you selected from multiple scholarly research resources.

What is Bayesian Network

Chapter 6
Educational and Social Dimensions of Digital Transformation in Organizations
A particular type of statistical model that represents a set of variables and their conditional dependencies. It is usually used to make previsions in a great variety of events.
Published in Chapter:
Artificial Intelligence Applied: Six Actual Projects in Big Organizations
Gaetano Bruno Ronsivalle (Università degli Studi di Verona, Italy) and Arianna Boldi (Wemole Srl, Italy)
Copyright: © 2019 |Pages: 30
DOI: 10.4018/978-1-5225-6261-0.ch006
Abstract
The purpose of the chapter is to present some real applications of the most advanced information technologies in complex adaptive systems like for-profit companies and organizations. In particular, the authors present the application of machine learning and artificial intelligence to support some of the activities that are strategic for an effective management of human resources. The tools have been applied to analyze the professional profiles (competencies, skills, knowledge, and activities), to evaluate the candidates for hiring and selection, to assess the competences in order to obtain a certification, or to prove the results of a training course. For each project, the authors provide a description of 1) the context, 2) the problem, 3) the solution implemented, 4) an analysis of the advantages and the limits of the solution. All these cases offer quantitative and qualitative data to sustain the thesis: artificial intelligence is a tool that can help humans managing the complexity levels of the so-called Anthropocene era we live in.
Full Text Chapter Download: US $37.50 Add to Cart
More Results
Chapter 41
Methodologies for Modeling Gene Regulatory Networks
A probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).
Published in Chapter: Methodologies for Modeling Gene Regulatory Networks; From: Encyclopedia of Information Science and Technology, Third Edition
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 276
History of Artificial Intelligence
A mathematic model in graphic form that represents a set of variables and their probabilistic independencies. It can be used, for example, to calculate the probability of a patient having a specific disease.
Published in Chapter: History of Artificial Intelligence; From: Encyclopedia of Information Science and Technology, Second Edition
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 32
Widely Applicable Multi-Variate Decision Support Model for Market Trend Analysis and Prediction with Case Study in Retail
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 2
Tracking Persons: A Survey
A bayesian network is a directed graphical model, which is useful when one want to express causal relationships between variables.
Published in Chapter: Tracking Persons: A Survey; From: Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 23
Systems Biology and Infectious Diseases
Represent a probabilistic relationships between a set of nodes: it is a directed graph which vertexes encode conditional independencies between the nodes.
Published in Chapter: Systems Biology and Infectious Diseases; From: Handbook of Research on Systems Biology Applications in Medicine
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 13
Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models
Probabilistic graphical model based on conditional probabilities, which contains connected conditional probability tables within chance nodes.
Published in Chapter: Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models; From: Handbook of Research on Swarm Intelligence in Engineering
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 29
Methods for Reverse Engineering of Gene Regulatory Networks
This refers to a probabilistic graphical network model defined by a set of random variables and a set of conditional probability distributions. These can be multinomial for discrete variables, Gaussian, for continuous variables, or others.
Published in Chapter: Methods for Reverse Engineering of Gene Regulatory Networks; From: Handbook of Research on Systems Biology Applications in Medicine
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 30
Data Integration for Regulatory Gene Module Discovery
or belief network is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. For example, a Bayesian network can be used to calculate the probability of a disease given the expression levels of certain genes. Expert knowledge is required in order to specify the structure and probabilistic dependencies among variables (genes and disease).
Published in Chapter: Data Integration for Regulatory Gene Module Discovery; From: Handbook of Research on Systems Biology Applications in Medicine
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 4
Inferring Gene Regulatory Networks from Genetical Genomics Data
Bayesian networks are directed probabilistic graphical models that represent conditional independence relationships among variables of interest.
Published in Chapter: Inferring Gene Regulatory Networks from Genetical Genomics Data; From: Handbook of Research on Computational Methodologies in Gene Regulatory Networks
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 5
Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model
is a probabilistic graphical model representing conditional independencies of random variables via a directed acyclic graph (DAG). A Bayesian network is specified by a graph structure and conditional probability distributions (CPDs) for each node, conditional upon its parents in the graph. Algorithms exist that perform inference and learning in Bayesian networks.
Published in Chapter: Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model; From: Handbook of Research on Computational Methodologies in Gene Regulatory Networks
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 15
Supplier Selection Improvement Process in an XYZ Company Through DMAIC
A probabilistic graphical model which uses Bayesian inference for the calculation of probability.
Published in Chapter: Supplier Selection Improvement Process in an XYZ Company Through DMAIC; From: Handbook of Research on Interdisciplinary Approaches to Decision Making for Sustainable Supply Chains
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 115
Integrating Ontologies and Bayesian Networks in Big Data Analysis
Graphical method that encodes probabilistic relationships among variables of interest. It is an artificial intelligence system that uses probabilistic information in making inferences.
Published in Chapter: Integrating Ontologies and Bayesian Networks in Big Data Analysis; From: Encyclopedia of Business Analytics and Optimization
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 147
Data Mining
A directed acyclic graph of nodes representing variables and arcs representing dependence relations among the variables.
Published in Chapter: Data Mining; From: Encyclopedia of Information Science and Technology, Second Edition
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 20
Machine Learning
A representation of knowledge in the form of a directed acyclic graph representing random variables as nodes and their conditional dependencies as edges.
Published in Chapter: Machine Learning; From: Encyclopedia of Information Science and Technology, Third Edition
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 9
Improving Decision Support Systems and Disruptive Technology Adoption With Analytical Serious Games
Bayesian Networks are Directed Acyclic Graphs that implement reasoning under uncertainty within the Bayesian probabilistic framework. They represent knowledge in graphs where node corresponds to a random variable and edge encode conditional probability distributions.
Published in Chapter: Improving Decision Support Systems and Disruptive Technology Adoption With Analytical Serious Games; From: Handbook of Research on Decision-Making Capabilities Improvement With Serious Games
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 12
To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model
Bayesian network is one of the graphical methodologies to build and represent models for problem solving with given data or expert opinion. This is a type of probabilistic graphical model which can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, reasoning, decision making under uncertainty etc.
Published in Chapter: To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model; From: Organizational Auditing and Assurance in the Digital Age
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 47
Automated Fault Management in Wireless Networks
A directed graph and a set of conditional probability functions that allow an efficient representation of a joint distribution over a set of random variables.
Published in Chapter: Automated Fault Management in Wireless Networks; From: Handbook of Research on Telecommunications Planning and Management for Business
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 80
Bayesian Network-Based Decision Support for Pest Management
A Bayesian network (BN) is a probabilistic graphical model for representing knowledge about an uncertain domain where each node corresponds to a random variable and each edge represents the conditional probability for the corresponding random variables.
Published in Chapter: Bayesian Network-Based Decision Support for Pest Management; From: Encyclopedia of Data Science and Machine Learning
Full Text Chapter Download: US $37.50 Add to Cart
Chapter 17
An Education Driven Model for Non-Communicable Diseases Care
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis.
Published in Chapter: An Education Driven Model for Non-Communicable Diseases Care; From: Handbook of Research on Advancing Health Education through Technology
Full Text Chapter Download: US $37.50 Add to Cart
eContent Pro Discount Banner
InfoSci OnDemandECP Editorial ServicesAGOSR