Results 21 to 30 of about 3,284,626 (292)
A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks
International Journal of Interactive Multimedia and Artificial Intelligence, 2014 Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that ...
Sho Fukuda +2 more
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On the Coherence of Probabilistic Relational Formalisms
Entropy, 2018 There are several formalisms that enhance Bayesian networks by including relations amongst individuals as modeling primitives. For instance, Probabilistic Relational Models (PRMs) use diagrams and relational databases to represent repetitive Bayesian ...
Glauber De Bona, Fabio G. Cozman
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Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks
Sensors, 2021 We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution ...
Federico Delussu +3 more
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Open Mathematics, 2018 The problem of structures learning in Bayesian networks is to discover a directed acyclic graph that in some sense is the best representation of the given database. Score-based learning algorithm is one of the important structure learning methods used to
Wang Jingyun, Liu Sanyang
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Differentiable PAC–Bayes Objectives with Partially Aggregated Neural Networks
Entropy, 2021 We make two related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC–Bayesian setting: (1) we show how averaging over an ensemble of stochastic neural networks enables a new class of partially ...
Felix Biggs, Benjamin Guedj
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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.
Clément L. Canonne +3 more
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Bayesian Learning of Markov Network Structure [PDF]
, 2006 We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naive Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar ...
Rish, Irina +3 more
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Bayesian networks for spatio-temporal integrated catchment assessment [PDF]
, 2010 Includes abstract.Includes bibliographical references (leaves 181-203).In this thesis, a methodology for integrated catchment water resources assessment using Bayesian Networks was developed.
Dondo, C
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CoRR, 2023 This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution.
Alex Graves +3 more
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Balanced Quantum-Like Bayesian Networks
Entropy, 2020 Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions.
Andreas Wichert +2 more
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