Results 41 to 50 of about 7,255,480 (346)
On Causal Explanations in Bayesian Networks [PDF]
, 2007 Explanations in Bayesian networks are usually probabilistic measures of how well a hypothesis is supported by observations. This observational based approach does not fulfill all the properties one would expect from an explanation. In particular, it does
Nielsen, Ulf Holm
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High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering
Frontiers in Genetics, 2019 Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait
Lingfei Wang +6 more
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Bayesian Neural Networks: An Introduction and Survey [PDF]
Case Studies in Applied Bayesian Data Science, 2020 Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing.
Ethan Goan, C. Fookes
semanticscholar +1 more source
Non-homogeneous dynamic Bayesian networks for continuous data [PDF]
, 2011 : Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed.
Husmeier, D. +5 more
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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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Uncertainty propagation for dropout-based Bayesian neural networks
Neural Networks, 2021 Uncertainty evaluation is a core technique when deep neural networks (DNNs) are used in real-world problems. In practical applications, we often encounter unexpected samples that have not seen in the training process.
Yuki Mae, Wataru Kumagai, T. Kanamori
semanticscholar +1 more source
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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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
openaire +5 more sources