Title :
Asymptotic normality of the posterior in relative entropy
Author :
Clarke, Bertrand S.
Author_Institution :
Dept. of Statistics, British Columbia Univ., Vancouver, BC, Canada
fDate :
1/1/1999 12:00:00 AM
Abstract :
We show that the relative entropy between a posterior density formed from a smooth likelihood and prior and a limiting normal form tends to zero in the independent and identically distributed case. The mode of convergence is in probability and in mean. Applications to code lengths in stochastic complexity and to sample size selection are discussed
Keywords :
codes; computational complexity; convergence of numerical methods; entropy; probability; signal sampling; asymptotic normality; code lengths; convergence; i.i.d. case; limiting normal form; mean; posterior density; probability; relative entropy; sample size selection; smooth likelihood; stochastic complexity; Bayesian methods; Convergence; Density measurement; Entropy; Maximum likelihood estimation; Parameter estimation; Size measurement; Source coding; Statistics; Stochastic processes;
Journal_Title :
Information Theory, IEEE Transactions on
