Results 261 to 270 of about 3,120,690 (293)

Causal mapping of psychological and occupational risk factors for suicidal ideation in psychiatric nurses using Bayesian networks: A multicenter cross-sectional study. [PDF]

PLoS One
Wang M, Yan Y, Yang W, He R, Zhao L, Dou Y, Tao Y, Yang X, Xiang Q, Ma X.   +9 more
europepmc   +1 more source

Scenario construction and evolutionary analysis of nonconventional public health emergencies based on Bayesian networks. [PDF]

Front Public Health
Zhu Y, Yang Q, Fu L, Cai C, Wang J, He L.   +5 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

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   +5 more sources

Some of the next articles are maybe not open access.

Related searches:

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   +2 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

Bayesian networks

Neurocomputing, 2010
What are Bayesian networks and why are their applications growing across all fields?
openaire   +3 more sources

Bayesian Networks for Inverse Inference in Manufacturing Bayesian Networks

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017
Physics 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, Sreedhar Reddy, Gautham B. P., Amol Joshi   +3 more
openaire   +1 more source