You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Conjugate Priors Represent Strong Pre-Experimental Assumptions
Eduardo Gutiérrez-Peña and Pietro Muliere
Scandinavian Journal of Statistics
Vol. 31, No. 2 (Jun., 2004), pp. 235-246
Published by: Wiley on behalf of Board of the Foundation of the Scandinavian Journal of Statistics
Stable URL: http://www.jstor.org/stable/4616826
Page Count: 12
You can always find the topics here!Topics: Statism, Entropy, Statistics, Mathematical independent variables, Random variables, Information theory, Statistical variance, Density, Statistical theories, Parametric models
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
It is well known that Jeffrey's prior is asymptotically least favorable under the entropy risk, i.e. it asymptotically maximizes the mutual information between the sample and the parameter. However, in this paper we show that the prior that minimizes (subject to certain constraints) the mutual information between the sample and the parameter is natural conjugate when the model belongs to a natural exponential family. A conjugate prior can thus be regarded as maximally informative in the sense that it minimizes the weight of the observations on inferences about the parameter; in other words, the expected relative entropy between prior and posterior is minimized when a conjugate prior is used.
Scandinavian Journal of Statistics © 2004 Board of the Foundation of the Scandinavian Journal of Statistics