Results 251 to 260 of about 2,020,572 (302)
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
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A canonical polyadic tensor basis for fast Bayesian estimation of multi-subject brain activation patterns. [PDF]
Front NeuroinformMiranda MF.
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Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data. [PDF]
Sci RepChakrabarti A, Ni Y, Mallick BK.
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Retraction: Epidemiological insights into paratuberculosis in camels in Saudi Arabia: Bayesian estimation of true prevalence and identification of risk factors. [PDF]
PLoS OnePLOS ONE Editors.
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IEEE Transactions on Education, 2009
Bayesian estimation of a threshold time (hereafter simply threshold) for the receipt of impulse signals is accomplished given the following: 1) data, consisting of the number of impulses received in a time interval from zero to one and the time of the largest time impulse; 2) a model, consisting of a uniform probability density of impulse time from ...
Steven C. Gustafson +4 more
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Bayesian estimation of a threshold time (hereafter simply threshold) for the receipt of impulse signals is accomplished given the following: 1) data, consisting of the number of impulses received in a time interval from zero to one and the time of the largest time impulse; 2) a model, consisting of a uniform probability density of impulse time from ...
Steven C. Gustafson +4 more
openaire +2 more sources
Bayesian estimation of unavailability
Reliability Engineering & System Safety, 2004Abstract This paper gives two Bayesian methods for estimating test-and-maintenance unavailability. Both unplanned and periodic maintenance are considered. One estimation method uses ‘detailed data,’ the individual outage times. The other method uses ‘summary data,’ totals of outage time and exposure time in various time periods such as calendar ...
Corwin L. Atwood, Max Engelhardt
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Statistics & Risk Modeling, 1996
Summary: The problem of Bayesian estimation of a signal contaminated by white noise is investigated. Restricting the unknown signal to a compact subset of \(L_2 ([0,1])\), the existence of a unique Bayes estimator for the squared-error-loss and its robustness with respect to the prior used are established; for sufficiently diffuse priors, the Bayes ...
Mukherjee, Kanchan, Majumdar, Suman
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Summary: The problem of Bayesian estimation of a signal contaminated by white noise is investigated. Restricting the unknown signal to a compact subset of \(L_2 ([0,1])\), the existence of a unique Bayes estimator for the squared-error-loss and its robustness with respect to the prior used are established; for sufficiently diffuse priors, the Bayes ...
Mukherjee, Kanchan, Majumdar, Suman
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Pattern Recognition, 1992
Abstract Bayesian estimation has many applications in computer vision. A frequent objection to Bayesian estimation is that the probability density functions (pdfs) involved are usually not known exactly. In fact, exact knowledge of the pdfs is not important; it often suffices to know the pdfs approximately. Furthermore, it may even suffice if we have
Mohamed Abdel-Mottaleb, Azriel Rosenfeld
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Abstract Bayesian estimation has many applications in computer vision. A frequent objection to Bayesian estimation is that the probability density functions (pdfs) involved are usually not known exactly. In fact, exact knowledge of the pdfs is not important; it often suffices to know the pdfs approximately. Furthermore, it may even suffice if we have
Mohamed Abdel-Mottaleb, Azriel Rosenfeld
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2019 18th European Control Conference (ECC), 2019
In the literature, the problem of frequency estimation is usually cast in a nonlinear parametric fashion. We show in this paper that it can also be formulated in a Bayesian nonparametric framework by assigning a uniform a priori probability distribution to the unknown frequency.
Giorgio Picci, Bin Zhu
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In the literature, the problem of frequency estimation is usually cast in a nonlinear parametric fashion. We show in this paper that it can also be formulated in a Bayesian nonparametric framework by assigning a uniform a priori probability distribution to the unknown frequency.
Giorgio Picci, Bin Zhu
openaire +2 more sources