Results 11 to 20 of about 7,255,480 (346)
Bayesian networks in neuroscience: A survey [PDF]
Frontiers in Computational Neuroscience, 2014 Bayesian networks are a type of probabilistic graphical modelslie at the intersection between statistics and machine learning.They have been shown to be powerful tools to encode dependence relationshipsamong the variables of a domain under uncertainty ...
Concha eBielza, Pedro eLarrañaga
doaj +5 more sources
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 +2 more
exaly +4 more sources
Bayesian networks in healthcare: Distribution by medical condition [PDF]
Artificial Intelligence in Medicine, 2020 Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare.
Scott Mclachlan +2 more
exaly +2 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
A Tutorial on Learning with Bayesian Networks [PDF]
Innovations in Bayesian Networks, 1998 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. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are ...
D. Heckerman
openaire +4 more sources
Bayesian Networks in Radiology. [PDF]
Radiol Artif Intell, 2023 A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acyclic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values.
Ma SX +6 more
europepmc +5 more sources
CausNet-partial: 'Partial Generational Orderings' based search for optimal sparse Bayesian networks via dynamic programming with parent set constraints. [PDF]
PLoS ONEIn our recent work, we developed a novel dynamic programming algorithm to find optimal Bayesian networks with parent set constraints. This 'generational orderings' based dynamic programming algorithm-CausNet-efficiently searches the space of possible ...
Nand Sharma, Joshua Millstein
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Bayesian networks in environmental modelling
Environmental Modelling and Software, 2011 Bayesian networks (BNs), also known as Bayesian belief networks or Bayes nets, are a kind of probabilistic graphical model that has become very popular to practitioners mainly due to the powerful probability theory involved, which makes them able to deal with a wide range of problems.The goal of this review is to show how BNs are being used in ...
Pedro A Aguilera +2 more
exaly +3 more sources
A Bayesian method for the induction of probabilistic networks from data
Machine Learning, 1992 Gregory F Cooper +2 more
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Cycles in Bayesian Networks [PDF]
International Journal of Electronics and Telecommunications, 2021 The article is devoted to some critical problems of using Bayesian networks for solving practical problems, in which graph models contain directed cycles.
Assem Shayakhmetova +4 more
doaj +1 more source