Results 31 to 40 of about 4,763,908 (250)
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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Journal of Industrial Engineering and Management, 2016 Purpose: Propose a modeling and analysis methodology based on the combination of Bayesian networks and Petri networks of the reverse logistics integrated the direct supply chain.
Faycal Mimouni, Abdellah Abouabdellah
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Integrative Bayesian Network Analysis of Genomic Data
Cancer Informatics, 2014 Rapid development of genome-wide profiling technologies has made it possible to conduct integrative analysis on genomic data from multiple platforms. In this study, we develop a novel integrative Bayesian network approach to investigate the relationships
Yang Ni +2 more
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Midsized-Rivers/Piebald-Madtom-Bayesian-belief-network: V1.2 Piebald Madtom Bayesian belief network
, 2023 <p>Repository contains supporting files for manuscript, Dunn et al. 2023. (in press at Ecosphere), "Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes." This manuscript presents a ...
Midsized-Rivers
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CAAI Transactions on Intelligence Technology, 2023 The battlefield situation changes rapidly because underwater targets' are concealment and the sea environment is uncertain. So, a great number of situation information greatly increase, which need to be dealt with in the course of scouting underwater ...
Yongqin Sun +3 more
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Bayesian Neural Networks [PDF]
, 2022 In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related and which are due to the neural network.
Tom Charnock +2 more
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Efficient utility-based clustering over high dimensional partition spaces [PDF]
, 2009 Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ +9 more
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Granger causality vs. dynamic Bayesian network inference: a comparative study [PDF]
, 2009 Background In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data.
Denby Katherine J +8 more
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Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 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.
Clément L. Canonne +3 more
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