Results 21 to 30 of about 7,597,672 (301)
Bayesian analysis of series system with dependent causes of failure
Statistical Theory and Related Fields, 2017 Most studies of series system assume the causes of failure are independent, which may not hold in practice. In this paper, dependent causes of failure are considered by using a Marshall–Olkin bivariate Weibull distribution.
Ancha Xu, Shirong Zhou
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Sensors, 2020 Deep learning has proven itself to be able to reduce the scanning time of Magnetic Resonance Imaging (MRI) and to improve the image reconstruction quality since it was introduced into Compressed Sensing MRI (CS-MRI).
Di Zhao, Feng Zhao, Yongjin Gan
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Reference priors for non-Normal two-sample problems [PDF]
Test, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fernández, C., Steel, M.F.J.
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Genome Biology, 2023 Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data.
Jiayu Su +10 more
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Reference priors for high energy physics [PDF]
Physical Review D, 2010 Bayesian inferences in high energy physics often use uniform prior distributions for parameters about which little or no information is available before data are collected. The resulting posterior distributions are therefore sensitive to the choice of parametrization for the problem and may even be improper if this choice is not carefully considered ...
Demortier, Luc +2 more
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The formal definition of reference priors
The Annals of Statistics, 2009 Reference analysis produces objective Bayesian inference, in the sense that inferential statements depend only on the assumed model and the available data, and the prior distribution used to make an inference is least informative in a certain information-theoretic sense.
Berger, James O. +2 more
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The Prior Can Often Only Be Understood in the Context of the Likelihood
Entropy, 2017 A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys’ priors, reference priors, maximum entropy priors, and weakly informative priors. These
Andrew Gelman +2 more
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Reference priors for discrete graphical models [PDF]
Biometrika, 2006 SUMMARY The combination of graphical models and reference analysis represents a powerful tool for Bayesian inference in highly multivariate settings. It is typically difficult to derive reference priors in complex problems. In this paper we present a suitable mixed para meterisation for a discrete decomposable graphical model and derive the ...
Consonni, Guido, Leucari, Valentina
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Estimation of a Covariance Matrix Using the Reference Prior
The Annals of Statistics, 1994 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Ruoyong, Berger, James O.
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Convolutional Neural Network‐Based Adaptive Localization for an Ensemble Kalman Filter
Journal of Advances in Modeling Earth Systems, 2023 Flow‐dependent background error covariances estimated from short‐term ensemble forecasts suffer from sampling errors due to limited ensemble sizes. Covariance localization is often used to mitigate the sampling errors, especially for high dimensional ...
Zhongrui Wang +4 more
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