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Inversion of Bayesian networks
International Journal of Approximate ReasoningVariational autoencoders and Helmholtz machines use a recognition network (encoder) to approximate the posterior distribution of a generative model (decoder). In this paper we study the necessary and sufficient properties of a recognition network so that it can model the true posterior distribution exactly.
Jesse van Oostrum +2 more
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Journal of Open Source SoftwareThe R package abn is a comprehensive tool for Bayesian Network (BN) analysis, a form of probabilistic graphical model. BNs are a type of statistical model that leverages the principles of Bayesian statistics and graph theory to provide a framework for representing complex multivariate data.
Delucchi, Matteo +3 more
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Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nir Friedman, Daphne Koller
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Reliability Analysis of Vehicle Braking System Based on Hyperellipsoidal Dynamic Bayesian Network
Applied SciencesBrake systems are subjected to various factors such as wear and fatigue over a long period of time. They bring a great challenge to the reliability analysis of the braking system.
Yingjie Tian, Jing Wen, Shubin Zheng
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Bayesian network–response regression [PDF]
Bioinformatics, 2017 Abstract Motivation There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited.
Lu Wang 0015 +3 more
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Evaluation of a Bayesian inference network for ligand-based virtual screening
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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Deep learning technology of Internet of Things Blockchain in distribution network faults
Journal of Intelligent Systems, 2022 Nowadays, the development of human society and daily life are inseparable from the power supply. Therefore, people also put forward higher requirements for the reliability of distribution network, but power companies can only passively deal with ...
Zhang Hong, Li Rui, Shi Chuncheng
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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 networks in neuroscience: a survey [PDF]
Frontiers in Computational Neuroscience, 2014 Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty.
Bielza Lozoya, María Concepción +1 more
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CoRR, 1997 A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network.
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