Results 11 to 20 of about 434,301 (280)
Bayesian Structure Learning for Climate Model Evaluation
Journal of Advances in Modeling Earth Systems, 2023 A Bayesian structure learning approach is employed to compare and contrast interactions between the major climate teleconnections over the recent past as revealed in reanalyses and climate model simulations from leading Meteorological Centers.
Terence J. O'Kane +2 more
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A Bayesian Generative Model for Learning Semantic Hierarchies [PDF]
Frontiers in Psychology, 2014 Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years.
Roni eMittelman +3 more
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Bayesian learning of coupled biogeochemical–physical models
Progress in Oceanography, 2023 45 pages; 18 figures; 2 ...
Abhinav Gupta, Pierre F.J. Lermusiaux
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Bayesian Model Averaging, Learning, and Model Selection* [PDF]
, 2013 Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models.
Evans, George W. +3 more
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A Bayesian Network Model of Causal Learning [PDF]
, 2022 Associationist theories of causal induction model learning as the acquisition of associative weights between cues and outcomes. An important deficit of this class of models is its insensitivity to the causal role of cues. A number of recent experimental findings have shown that human learners differentiate between cues that represent causes and cues ...
Waldmann, Michael R., Martignon, Laura
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Non-Bayesian Social Learning With Uncertain Models [PDF]
IEEE Transactions on Signal Processing, 2020 Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network. In this framework, each agent iteratively forms and communicates beliefs about an unknown state of the world with their neighbors using a learning rule.
James Z. Hare +3 more
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Sparse Bayesian Modeling With Adaptive Kernel Learning [PDF]
IEEE Transactions on Neural Networks, 2009 Sparse kernel methods are very efficient in solving regression and classification problems. The sparsity and performance of these methods depend on selecting an appropriate kernel function, which is typically achieved using a cross-validation procedure.
Tzikas, D. G. +2 more
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Hierarchical Bayesian Models of Subtask Learning
Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015 The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task,
Jeromy, Anglim, Sarah K A, Wynton
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Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory
Entropy, 2020 Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training ...
Sergey Oladyshkin +3 more
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Neural surprise in somatosensory Bayesian learning.
PLoS Computational Biology, 2021 Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles.
Sam Gijsen +4 more
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