Results 21 to 30 of about 2,475,679 (305)
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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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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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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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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A Tutorial on Learning with Bayesian Networks [PDF]
Innovations in Bayesian Networks, 1999 A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis.
D. Heckerman
semanticscholar +1 more source
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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Performance-Oriented Model Learning for Data-Driven MPC Design [PDF]
IEEE Control Systems Letters, 2019 Model predictive control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs.
D. Piga +3 more
semanticscholar +1 more source
Energies, 2021 Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process.
Xue-bo Jin +6 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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