Results 31 to 40 of about 862,083 (298)
Bayesian Inference and Fuzzy Information
, 2018 In standard Bayesian inference, a-priori distributions are standard probability distributions. Bayes´ theorem formulates the transition from the a-priori distribution of the stochastic quantity, which describes the parameter of interest, to the a ...
Sunanta, Owat, Viertl, Reinhard
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Skill rating by Bayesian inference [PDF]
2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009 Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess.
Giuseppe Di Fatta +2 more
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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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Deep bootstrap for Bayesian inference
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023 For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior. We focus on situations when the parameter of interest has been emancipated from the likelihood and is linked to data directly through a loss function.
Lizhen Nie, Veronika Ročková
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Bayesian Inference for Acoustic Direction of Arrival Analysis Using Spherical Harmonics
Entropy, 2019 This work applies two levels of inference within a Bayesian framework to accomplish estimation of the directions of arrivals (DoAs) of sound sources. The sensing modality is a spherical microphone array based on spherical harmonics beamforming.
Ning Xiang, Christopher Landschoot
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Bayesian inference for dynamical systems
Infectious Disease Modelling, 2020 Bayesian inference is a common method for conducting parameter estimation for dynamical systems. Despite the prevalent use of Bayesian inference for performing parameter estimation for dynamical systems, there is a need for a formalized and detailed ...
Weston C. Roda
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The material-weight illusion is a Bayes-optimal percept under competing density priors [PDF]
PeerJ, 2018 The material-weight illusion (MWI) is one example in a class of weight perception illusions that seem to defy principled explanation. In this illusion, when an observer lifts two objects of the same size and mass, but that appear to be made of different ...
Megan A.K. Peters +2 more
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Lévy Walk in Swarm Models Based on Bayesian and Inverse Bayesian Inference
Computational and Structural Biotechnology Journal, 2021 While swarming behavior is regarded as a critical phenomenon in phase transition and frequently shows the properties of a critical state such as Lévy walk, a general mechanism to explain the critical property in swarming behavior has not yet been found ...
Yukio-Pegio Gunji +5 more
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Bayesian inference with optimal maps [PDF]
Journal of Computational Physics, 2012 66 pages, 26 figures. Minor revisions and improvements throughout.
Tarek A. El-Moselhy, Youssef M. Marzouk
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Quantum inference on Bayesian networks [PDF]
Physical Review A, 2014 Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values $e$ of evidence variables. Classically, a single unbiased sample is obtained from a Bayesian network on $n$ variables with at most $m$ parents per node in
Low, Guang Hao +2 more
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