Results 311 to 320 of about 2,810,332 (372)
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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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Quality and Reliability Engineering International, 2020
In this paper, expected Bayesian (E‐Bayesian) estimation for the simple step–stress model based on type‐II censoring scheme is considered. The case of exponential distribution for the underlying lifetimes is considered assuming a cumulative exposure ...
M. Nassar, Hassan M. Okasha, M. Albassam
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In this paper, expected Bayesian (E‐Bayesian) estimation for the simple step–stress model based on type‐II censoring scheme is considered. The case of exponential distribution for the underlying lifetimes is considered assuming a cumulative exposure ...
M. Nassar, Hassan M. Okasha, M. Albassam
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Bayesian dynamic system estimation
53rd IEEE Conference on Decision and Control, 2014This paper is directed at developing methods for delivering Bayesian estimates of dynamic system parameters, and functions of them (such as frequency response), for general problems. There are several motivations for the work. One is that due to computational load problems, such methods for Bayesian estimation do not currently exist.
Ninness, Brett +2 more
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Nearly Exact Bayesian Estimation of Non-linear No-Arbitrage Term-Structure Models*
Journal of Financial Econometrics, 2018We propose a general method for the Bayesian estimation of a very broad class of non-linear no-arbitrage term-structure models. The main innovation we introduce is a computationally efficient method, based on deep learning techniques, for approximating
Marcello Pericoli, Marco Taboga
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Estimation in Bayesian Disease Mapping
Biometrics, 2004SummaryRecent work on Bayesian inference of disease mapping models discusses the advantages of the fully Bayesian (FB) approach over its empirical Bayes (EB) counterpart, suggesting that FB posterior standard deviations of small‐area relative risks are more reflective of the uncertainty associated with the relative risk estimation than counterparts ...
MacNab, Ying C. +3 more
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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
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IEEE Transactions on Automatic Control, 1988
Taking the Bayesian approach in solving the discrete-time parameter estimation problem has two major results: the unknown parameters are legitimately included as additional system states, and the computational objective becomes calculations of the entire posterior density instead of just its first few moments.
Kramer, Stuart C., Sorenson, Harold W.
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Taking the Bayesian approach in solving the discrete-time parameter estimation problem has two major results: the unknown parameters are legitimately included as additional system states, and the computational objective becomes calculations of the entire posterior density instead of just its first few moments.
Kramer, Stuart C., Sorenson, Harold W.
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Nonparametric Bayesian Interval Estimation
Biometrika, 1979SUMMARY Bayesian confidence bands for a distribution function are converted into confidence intervals for specified population quantiles and also for the mean. Bayesian tolerance limits are similarly constructed.
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2016
This chapter discusses Bayesian inference as a method for uncertainty quantification (UQ) in parameter estimation problems. The need for an UQ approach is motivated by investigating the deterministic parameter estimation problem; afterward, the specifics of the Bayesian parameter estimation approach are elaborated.
E. Simoen, G. Lombaert
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This chapter discusses Bayesian inference as a method for uncertainty quantification (UQ) in parameter estimation problems. The need for an UQ approach is motivated by investigating the deterministic parameter estimation problem; afterward, the specifics of the Bayesian parameter estimation approach are elaborated.
E. Simoen, G. Lombaert
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