Results 21 to 30 of about 3,103,450 (291)
PLoS Computational Biology, 2021 In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data.
Ann-Kristin Becker +9 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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Bayesian Network Classifiers [PDF]
Machine Learning, 1997 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nir Friedman +2 more
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Probabilistic Graph Models (PGMs) for Feature Selection in Time Series Analysis and Forecasting
JISR on Computing, 2021 Time series or longitudinal analysis has a very important aspect in the field of research. Day by day new and better analyses are getting developed in this field.
Syed Ali Raza Naqvi
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CoRR, 2023 Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decision making under uncertainty. Most notably, Bayesian agents do not face an exploration/exploitation dilemma, a major pathology of frequentist methods. However theoretical understanding of model-free approaches is lacking.
Mattie Fellows +3 more
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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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Mathematical and Computational Applications, 2020 We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed ...
Vicente-Josué Aguilera-Rueda +2 more
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Brain-Inspired Hardware Solutions for Inference in Bayesian Networks
Frontiers in Neuroscience, 2021 The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due to the substantial resources required by floating ...
Leila Bagheriye, Johan Kwisthout
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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 [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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