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The Uniform Distribution as a Universal Prior
IEEE Transactions on Information Theory, 2004In this correspondence, we discuss the properties of the uniform prior as a universal prior, i.e., a prior that induces a mutual information that is simultaneously close to the capacity for all channels. We determine bounds on the amount of the mutual information loss in using the uniform prior instead of the capacity-achieving prior. Specifically, for
N. Shulman, M. Feder
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A Note on the Uniform Prior Distribution for Reliability
IEEE Transactions on Reliability, 1970The uniform prior distribution is a mathematically acceptable prior distribution for reliability R(t) = exp (-?t). Certain other considerations, however, lead to the conclusion that the uniform prior distribution on R(t) should be used with extreme caution.
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Uniform Priors for Impulse Responses
Econometrica, 2022There has been a call for caution regarding the standard procedure for Bayesian inference in set‐identified structural vector autoregressions on the grounds that the common practice of using a uniform prior over the set of orthogonal matrices induces a non‐uniform prior for individual impulse responses or other quantities of interest.
Arias, Jonas E. +2 more
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DERIVING PROPER UNIFORM PRIORS FOR REGRESSION COEFFICIENTS
AIP Conference Proceedings, 2011In problems of model comparison between competing regression models, one must take care not to use improper priors. Improper priors introduce inverse infinities in the evidence factors, which do not cancel if one proceeds to compute the posterior probabilities of models which have different numbers of regression coefficients.
N. van Erp +4 more
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Bayesian classification and feature reduction using uniform dirichlet priors
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2003In this paper, a method of classification referred to as the Bayesian data reduction algorithm (BDRA) is developed. The algorithm is based on the assumption that the discrete symbol probabilities of each class are a priori uniformly Dirichlet distributed, and it employs a "greedy" approach (which is similar to a backward sequential feature search) for ...
R R, Lynch, P K, Willett
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Strong Inconsistency from Uniform Priors
Journal of the American Statistical Association, 1976Behavior of this type will be called strong inconsistency. Box and Tiao [2, p. 310] might, apparently, tolerate such behavior by the simple device of ignoring (1.3), which, based on a sampling distribution, is regarded as irrelevant to the Bayesian inference. In support orf this, Box and Tiao [2, p.
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Bayesian Learning for Classification using a Uniform Dirichlet Prior
2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019In Bayesian learning, designs based on noninformative priors are appropriate when the user cannot confidently identify the data-generating distribution. While such learners cannot achieve the performance of those based on a well-matched subjective prior, they impart a robustness against poor prior selection.
Paul Rademacher, Milos Doroslovacki
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Uniform priors on convex sets improve risk
Statistics & Probability Letters, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the implicit uniform BIC prior [PDF]
Economics Bulletin, 2014 I show how to find the uniform prior implicit in using the Bayesian Information Criterion to consider a hypothesis about a single normally distributed parameter. The ratio of the width of the implicit prior to the standard deviation of the parameter estimate is √2πn for large samples.
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Strong Inconsistency from Uniform Priors: Comment
Journal of the American Statistical Association, 1976+6 more sources