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Showing 1 to 6 of 6 for “"Jeffreys' prior"”.
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Inference for the ratio of two exponential parameters using a Bayesian approach
In this dissertation the maximal data information prior and the probability matching prior for the ratio of two exponential parameters will be derived. The method by Datta and Ghosh (1995) will be used to derive the probability matching prior and the method proposed by Zellner (1971) will be used …
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Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… this part is to derive objective/non-informative priors for the parameterizations and use these priors to build up constructive random posteriors of the Kullback-Liebler (KL) divergence of the two multivariate normal populations, which is proportional to the distance between the two means, …
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Seismic Data Conditioning and Inversion with Bayesian Methods and Dynamic Time-Warping
… with two sparsity-promoting hierarchical prior distributions (Normal-Jeffreys and automatic relevance determination). The Normal-Jeffreys prior computes a sparse model that estimates observational noise variance which regularizes the solution. Moreover, Amplitude variation with offset …
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On the saddle-point solution and the large-coalition behavior of fingerprinting games
We study a fingerprinting game in which the number of colluders and the collusion channel are unknown. The encoder embeds fingerprints into a host sequence and provides the decoder with the capability to trace back pirated copies to the colluders. Fingerprinting capacity has recently been derived …
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Application Of Statistical Methods In Risk And Reliability
… by objective Bayesian methods and use the Jeffreys noninformative prior. Performance of the resulting confidence intervals is studied via Monte Carlo simulations and compared to the performance of nonparametric confidence intervals based on binomial proportion. In addition, techniques for …
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Landscapes of Finite Information
… to a method for constructing robust Bayesian priors that still allows for a large degree of subjective input, thus, in a sense, bridging the divide between subjective and objective Bayesianism.