Bayesian Networks and Influence Diagrams

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Serval ID
serval:BIB_74C1A2925AFF
Type
A part of a book
Publication sub-type
Chapter: chapter ou part
Collection
Publications
Institution
Title
Bayesian Networks and Influence Diagrams
Title of the book
Encyclopedia of Forensic Sciences, Third Edition
Author(s)
Biedermann Alex, Taroni Franco
Publisher
Elsevier
ISBN
9780128236789
Publication state
Published
Issued date
2023
Pages
271-280
Language
english
Abstract
Bayesian networks are graphical models that have been developed in the field of artificial intelligence as a framework to help researchers and practitioners apply probability theory to inference problems of substantive size as encountered in real-world applications. Influence diagrams (Bayesian decision networks) extend Bayesian networks to a modeling environment for coherent decision analysis under uncertainty. This article provides an overview of these methods and explains their contribution to the body of formal methods for the study, development and implementation of probabilistic procedures for assessing the probative value of scientific evidence and the coherent analysis of related questions of decision-making.
Keywords
Bayes’ theorem, Bayesian network, Decision analysis, Decision theory, DNA evidence, Evidence evaluation and interpretation, Influence diagram, Probability theory, Uncertainty, Value of evidence
Create date
11/11/2022 21:01
Last modification date
12/11/2022 7:11
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