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Moving Beyond Noninformative Priors: Why and How to Choose Weakly Informative Priors in Bayesian Analyses [PDF]
Oikos, 2019 Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods.
Nathan P. Lemoine
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On the Invariance of Noninformative Priors
Annals of Statistics, 1996 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Malay Ghosh, Gauri Datta
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Some Remarks on Noninformative Priors
Journal of the American Statistical Association, 1995Abstract This article focuses primarily on a comparison between the reference priors of Berger and Bernardo and the reverse reference priors suggested by J. K. Ghosh. Sufficient conditions are given that provide agreement between the two classes of priors. Several examples are given showing the agreement or disagreement between the two.
Malay Ghosh, Gauri Datta
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Noninformative Priors and Nuisance Parameters
Journal of the American Statistical Association, 1993Abstract We study the conflict between priors that are noninformative for a parameter of interest versus priors that are noninformative for the whole parameter. Our investigation leads us to maximize a functional that has two terms: an asymptotic approximation to a standardized expected Kullback-Leibler distance between the marginal prior and marginal ...
Bertrand Clarke, Larry Wasserman
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Constrained Noninformative Priors in Risk Assessment
Reliability Engineering and System Safety, 1996Abstract A constrained noninformative prior distribution, a generalization of the Jeffreys noninformative prior, is defined for a single unknown parameter as the distribution corresponding to the maximum entropy distribution, subject to the assumed constraint(s), in the transformed model where the unknown parameter is approximately a location ...
Corwin L Atwood
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Hidden Dangers of Specifying Noninformative Priors
American Statistician, 2012“Noninformative” priors are widely used in Bayesian inference. Diffuse priors are often placed on parameters that are components of some function of interest. That function may, of course, have a prior distribution that is highly informative, in contrast to the joint prior placed on its arguments, resulting in unintended influence on the posterior for ...
James D. Stamey
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Noninformative priors for the nested design
Journal of Statistical Planning and Inference, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kim, Dal Ho +2 more
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Noninformative priors for inferences in exponential regression models
Biometrika, 1991Reference priors [\textit{J. O. Berger} and \textit{J. M. Bernardo}, J. Am. Stat. Assoc. 84, No. 405, 200-207 (1989; Zbl 0682.62018)] are used to estimate the regression parameter \(\rho\) in the exponential regression model given by \[ Y_{ij}\sim N(\alpha+\beta\rho^{x+x_ ia},\sigma^ 2), \] where \(\alpha,\beta\in R\), \(0 < \rho < 1\), \(x\) and \(a\)
Ye, Keying, Berger, James O.
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Bayesian Estimation of the Number of Species Using Noninformative Priors
Biometrical Journal, 2008AbstractConsider a sample of animal abundances collected from one sampling occasion. Our focus is in estimating the number of species in a closed population. In order to conduct a noninformative Bayesian inference when modeling this data, we derive Jeffreys and reference priors from the full likelihood.
John Bunge
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A note on noninformative priors for Weibull distributions
Journal of Statistical Planning and Inference, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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