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Asymptotically Informative Prior for Bayesian Analysis


Author Info

  • Ao Yuan

    (Howard University, Washington DC, USA)

  • Jan G. de Gooijer

    (University of Amsterdam)


In classical Bayesian inference the prior is treated as fixed, it is asymptotically negligible,thus any information contained in the prior is ignored from the asymptotic first order result.However, in practice often an informative prior is summarized from previous similar or the samekind of studies, which contains non-negligible information for the current study. Here, differentfrom traditional Bayesian point of view, we treat such prior to be non-fixed. In particular,we give the data sizes used in previous studies for the prior the same status as the size of thecurrent dataset, viewing both sample sizes as increasing to infinity in the asymptotic study.Thus the prior is asymptotically non-negligible, and its original effects are ressumed under thisview. Consequently, Bayesian inference using such prior is more efficient, as it should be, thanthat regarded under the existing setting. We study some basic properties of Bayesian estimatorsusing such priors under convex losses and the 0—1 loss, and illustrate the method by an examplevia simulation.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-130/4.

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Date of creation: 15 Sep 2011
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Handle: RePEc:dgr:uvatin:20110130

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Keywords: Asymptotically informative prior; asymptotic efficiency; Bayes estimator; information bound; maximum likelihood estimator;

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  1. Bradley Efron, 2005. "Bayesians, Frequentists, and Scientists," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1-5, March.
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