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Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings. [PDF]
PLoS Computational Biology, 2011 Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty.
Elise Payzan-LeNestour, Peter Bossaerts
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A Noise-Robust Fast Sparse Bayesian Learning Model [PDF]
Communications in Statistics - Simulation and Computation, 2020 This paper utilizes the hierarchical model structure from the Bayesian Lasso in the Sparse Bayesian Learning process to develop a new type of probabilistic supervised learning approach.
Helgøy, Ingvild M., Li, Yushu
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On Sequential Bayesian Inference for Continual Learning
Entropy, 2023 Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
Samuel Kessler +4 more
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Opinion Dynamics with Bayesian Learning
Complexity, 2020 Bayesian learning is a rational and effective strategy in the opinion dynamic process. In this paper, we theoretically prove that individual Bayesian learning can realize asymptotic learning and we test it by simulations on the Zachary network.
Aili Fang +3 more
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Using consensus bayesian network to model the reactive oxygen species regulatory pathway. [PDF]
PLoS ONE, 2013 Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data ...
Liangdong Hu, Limin Wang
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A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus
BMC Medical Research Methodology, 2023 Objective To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN ...
Yue Lin +4 more
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Probabilistic Predictions with Federated Learning
Entropy, 2020 Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods ...
Adam Thor Thorgeirsson, Frank Gauterin
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Frontiers in Neuroscience, 2014 Computational models of learning have proved largely successful in characterising potentialmechanisms which allow humans to make decisions in uncertain and volatile contexts.
Elaine eDuffin +3 more
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Mapping shape to visuomotor mapping: learning and generalisation of sensorimotor behaviour based on contextual information. [PDF]
PLoS Computational Biology, 2015 Humans can learn and store multiple visuomotor mappings (dual-adaptation) when feedback for each is provided alternately. Moreover, learned context cues associated with each mapping can be used to switch between the stored mappings.
Loes C J van Dam, Marc O Ernst
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Non-Bayesian Social Learning With Imperfect Private Signal Structure
IEEE Access, 2019 As one of the classic models that describe the belief dynamics over social networks, a non-Bayesian social learning model assumes that members in the network possess accurate signal knowledge through the process of Bayesian inference.
Sannyuya Liu +3 more
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