Results 271 to 280 of about 118,631 (311)
Use of Bayesian networks in Brazil high school educational database: analysis of the impact of COVID-19 on ENEM in Pará between 2019 and 2022. [PDF]
Front Big DataSantos SMD +3 more
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Comparing Causal Bayesian Networks Estimated from Data. [PDF]
Entropy (Basel)Ma S, Tourani R.
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Semiparametric Bayesian networks
Information Sciences, 2022 We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones.
Concha Bielza +2 more
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Bayesian networks in reliability [PDF]
Reliability Engineering and System Safety, 2007 Over the last decade, Bayesian networks (BNs) have become a popular tool for modelling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. In this paper we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applications ...
Helge Langseth, Luigi Portinale
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Wiley Interdisciplinary Reviews: Computational Statistics, 2009
AbstractBayesian networks are defined, and the chain rule for Bayesian networks is stated. Outlines of algorithms provided: inference in Bayesian networks, sensitivity analysis, EM for parameter learning, and learning structure. Copyright © 2009 John Wiley & Sons, Inc.This article is categorized under:Statistical and Graphical Methods of Data ...
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AbstractBayesian networks are defined, and the chain rule for Bayesian networks is stated. Outlines of algorithms provided: inference in Bayesian networks, sensitivity analysis, EM for parameter learning, and learning structure. Copyright © 2009 John Wiley & Sons, Inc.This article is categorized under:Statistical and Graphical Methods of Data ...
exaly +2 more sources
Communications of the ACM, 1995
This brief tutorial on Bayesian networks serves to introduce readers to some of the concepts, terminology, and notation employed by articles in this special section. In a Bayesian network, a variable takes on values from a collection of mutually exclusive and collective exhaustive states.
David Heckerman, Michael P. Wellman
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This brief tutorial on Bayesian networks serves to introduce readers to some of the concepts, terminology, and notation employed by articles in this special section. In a Bayesian network, a variable takes on values from a collection of mutually exclusive and collective exhaustive states.
David Heckerman, Michael P. Wellman
openaire +1 more source
Neurocomputing, 2010
What are Bayesian networks and why are their applications growing across all fields?
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What are Bayesian networks and why are their applications growing across all fields?
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Bayesian Networks for Inverse Inference in Manufacturing Bayesian Networks
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017Physics based simulations of manufacturing processes are used for prediction of material properties and defects in a number of industrial applications. However, a practising engineer often requires the solution to an "inverse problem" - prediction of inputs for the desired outcome.
Avadhut Sardeshmukh +3 more
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