Results 21 to 30 of about 471,788 (278)
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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Non-Bayesian social learning [PDF]
Games and Economic Behavior, 2012 We develop a dynamic model of opinion formation in social networks when the information required for learning a payoff-relevant parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences.
Ali Jadbabaie +3 more
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Bayesian Quantum Neural Networks
IEEE Access, 2022 The astounding acceleration in Artificial Intelligence and Quantum Computing advances naturally gives rise to a line of research, which unrolls the potential advantages of quantum computing on classical Machine Learning tasks, known as Quantum Machine ...
Nam Nguyen, Kwang-Cheng Chen
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Probabilistic Predictions with Federated Learning
Entropy, 2020 Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods ...
Adam Thor Thorgeirsson, Frank Gauterin
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Deep learning does not replace Bayesian modeling: Comparing research use via citation counting
Applied AI Letters, 2022 One could be excused for assuming that deep learning had or will soon usurp all credible work in reasoning, artificial intelligence, and statistics, but like most “meme” class broad generalizations the concept does not hold up to scrutiny.
Breck Baldwin
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A Bayesian network structure learning method for optimizing ordering search operator
Xibei Gongye Daxue Xuebao, 2023 Local search algorithm in ordering space is a good method which can effectively improve the efficiency of bayesian network structure learning. However, the existing algorithms usually have problems such as insufficient order optimization, low learning ...
JIA Liuna +4 more
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Bayesian learning with Wasserstein barycenters
ESAIM: Probability and Statistics, 2022 We introduce and study a novel model-selection strategy for Bayesian learning, based on optimal transport, along with its associated predictive posterior law: the Wasserstein population barycenter of the posterior law over models. We first show how this estimator, termed Bayesian Wasserstein barycenter (BWB), arises naturally in a general, parameter ...
Backhoff-Veraguas, Julio Daniel +3 more
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Time-frequency doubly selective channel estimation based on compressed sensing
上海师范大学学报. 自然科学版, 2017 In this paper,considering time-frequency doubly selective channel,we utilize the channel's time correlation that the channel coefficientscorresponding tothe neighboring instants have a strong correlation.And we present a linear approximation method,which
Tu Yuliang +3 more
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Learning to play Bayesian games [PDF]
Games and Economic Behavior, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dekel, Eddie +2 more
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Reverse engineering of genetic networks with Bayesian networks [PDF]
, 2003 This paper provides a brief introduction to learning Bayesian networks from gene-expression data. The method is contrasted with other approaches to the reverse engineering of biochemical networks, and the Bayesian learning paradigm is briefly described ...
Husmeier, D.
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