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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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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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Semiparametric Bayesian inference in multiple equation models [PDF]
, 2005 This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components.
Koop, G.M. +5 more
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The predictive view of Bayesian inference [PDF]
, 2022 This thesis considers the direct connection between the prediction of future observations and Bayesian inference. Using prediction as a guide, we generalize the Bayesian framework and introduce new methodologies for parameter inference and model ...
Fong, Edwin
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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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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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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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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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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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