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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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Evaluation of a Bayesian inference network for ligand-based virtual screening [PDF]
Journal of Cheminformatics, 2009 Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query.
Chen Beining +2 more
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On adaptive Bayesian inference
Electronic Journal of Statistics, 2008 We study the rate of Bayesian consistency for hierarchical priors consisting of prior weights on a model index set and a prior on a density model for each choice of model index. Ghosal, Lember and Van der Vaart [2] have obtained general in-probability theorems on the rate of convergence of the resulting posterior distributions.
Xing, Yang
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Bayesian inference for CoVaR [PDF]
, 2013 Recent financial disasters emphasised the need to investigate the consequence associated with the tail co-movements among institutions; episodes of contagion are frequently observed and increase the probability of large losses affecting market participants' risk capital.
BERNARDI, MAURO +2 more
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Bayesian statistical inference
Statistica, 2017 This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D.
Bruno De Finetti
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Network Plasticity as Bayesian Inference
PLOS Computational Biology, 2015 33 pages, 5 figures, the supplement is available on the author's web page http://www.igi.tugraz.at ...
Stefan Habenschuss +3 more
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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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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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