Results 221 to 230 of about 399,768 (265)

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation. [PDF]

JMIR Ment Health
Maekawa E, Grua EM, Nakamura CA, Scazufca M, Araya R, Peters T, van de Ven P.   +6 more
europepmc   +1 more source

Lung Cancer Detection Using Bayesian Networks: A Retrospective Development and Validation Study on a Danish Population of High-Risk Individuals. [PDF]

Cancer Med
Henriksen MB, Van Daalen F, Wee L, Hansen TF, Jensen LH, Brasen CL, Hilberg O, Bermejo I.   +7 more
europepmc   +1 more source

Real-time monitoring and prediction of remote operator fatigue in plateau deep mining based on dynamic Bayesian networks. [PDF]

Sci Rep
Chen S, Pan L, Xu K, Li X, Zuo Y, Zhou Z, Li B, Dai Z, Li Z.   +8 more
europepmc   +1 more source

Modelling Interventions to Combat Antibacterial Resistance in East Africa Using Causal Bayesian Networks

Ke X, Smith V, Mshana SE, Asiimwe B, Neema S, Kiiru J, Mushi MF, Mmbaga BT, Mwanga JR, Kibiki G, Stelling J, Gillespie SH, Green DL, Benvari S, Sabiiti W, Kesby M, Lynch AG, Sandeman A, Sloan DJ, Holden MT, Keenan K, HATUA Consortium.   +21 more
europepmc   +1 more source

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.
David Atienza, Concha Bielza, Pedro Larranaga   +2 more
exaly   +4 more sources

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Bayesian networks

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 ...
Finn V Jensen
exaly   +4 more sources

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
exaly   +3 more sources

Bayesian networks

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
openaire   +1 more source