Results 11 to 20 of about 147,580 (306)
Approximate Bayesian Inference [PDF]
Entropy, 2020 This is the Editorial article summarizing the scope of the Special Issue: Approximate Bayesian Inference.
Pierre Alquier
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Universal Darwinism As a Process of Bayesian Inference
Frontiers in Systems Neuroscience, 2016 Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these ...
John Oberon Campbell
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Overview of Research on Bayesian Inference and Parallel Tempering [PDF]
Jisuanji kexue, 2023 Bayesian inference is one of the main problems in statistics.It aims to update the prior knowledge of the probability distribution model based on the observation data.For the posterior probability that cannot be observed or is difficult to directly ...
ZHAN Jin, WANG Xuefei, CHENG Yurong, YUAN Ye
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Connecting the free energy principle with quantum cognition
Frontiers in Neurorobotics, 2022 It appears that the free energy minimization principle conflicts with quantum cognition since the former adheres to a restricted view based on experience while the latter allows deviations from such a restricted view.
Yukio-Pegio Gunji +2 more
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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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A Bayesian inference model for metamemory. [PDF]
Psychological Review, 2021 The dual-basis theory of metamemory suggests that people evaluate their memory performance based on both processing experience during the memory process and their prior beliefs about overall memory ability. However, few studies have proposed a formal computational model to quantitatively characterize how processing experience and prior beliefs are ...
Xiao Hu +7 more
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CoRR, 2021 The right to be forgotten has been legislated in many countries but the enforcement in machine learning would cause unbearable costs: companies may need to delete whole models learned from massive resources due to single individual requests. Existing works propose to remove the knowledge learned from the requested data via its influence function which ...
Shaopeng Fu +3 more
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Adaptive User Interfaces and the Use of Inference Methods
Computational Science and Techniques, 2021 Bayesian Networks are used to model a user's behaviour. There is not much research on the use of Frequentist Inference to accomplish this same task.
Rachelle Barrette, Ratvinder Grewal
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On the Brittleness of Bayesian Inference [PDF]
SIAM Review, 2015 20 pages, 2 figures. To appear in SIAM Review (Research Spotlights).
Houman Owhadi +2 more
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Randomization-based, Bayesian inference of causal effects
Journal of Causal Inference, 2023 Bayesian causal inference in randomized experiments usually imposes model-based structure on potential outcomes. Yet causal inferences from randomized experiments are especially credible because they depend on a known assignment process, not a ...
Leavitt Thomas
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