Results 11 to 20 of about 807,381 (349)
Evaluation of a Bayesian inference network for ligand-based virtual screening [PDF]
Journal of Cheminformatics, 2009 Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query.
Chen Beining +2 more
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Bayesian networks and decision trees in the diagnosis of female urinary incontinence [PDF]
, 2002 This study compares the effectiveness of Bayesian networks versus Decision Trees in modeling the Integral Theory of Female Urinary Incontinence diagnostic algorithm. Bayesian networks and Decision Trees were developed and trained using data from 58 adult
Margie Hunt +3 more
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Bayesian network for predicting mandibular third molar extraction difficulty [PDF]
BMC Oral HealthBackground This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output ...
Tian Meng +3 more
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Network Plasticity as Bayesian Inference
PLOS Computational Biology, 2015 General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing.
Habenschuss, Stefan +3 more
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Testing Bayesian Networks [PDF]
IEEE Transactions on Information Theory, 2020 This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks -- the prototypical family of directed graphical models. A Bayesian network is defined by a directed acyclic graph, where we associate a random variable with each node.
Clement L. Canonne +3 more
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IEEE Access, 2022 To address the problem of low efficiency of the existing hill-climbing algorithm in Bayesian network structure learning, this paper proposes a Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints.
Haoran Liu +7 more
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Bayesian Networks in Radiology [PDF]
Radiology: Artificial Intelligence, 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.
Shawn X. Ma +6 more
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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.
Atienza González, David +2 more
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Using consensus bayesian network to model the reactive oxygen species regulatory pathway. [PDF]
PLoS ONE, 2013 Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data ...
Liangdong Hu, Limin Wang
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Survey of Research on Non-homogeneous Gene Regulatory Network Models [PDF]
Jisuanji kexue yu tansuo, 2023 In the field of bioinformatics, the construction of gene regulatory networks is crucial. In recent years, non-homogeneous dynamic Bayesian networks have become a common modeling tool for learning gene regulatory networks from gene expression time-series ...
ZHANG Qianqian, HU Chunling, ZHANG Jiayao, LI Dawei, SHAO Mingyi
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