Title :
Least-informative Bayesian prior distributions for finite samples based on information theory
Author :
Spall, James C. ; Hill, Stacy D.
Author_Institution :
Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA
Abstract :
A procedure, based on Shannon information theory, for producing least-informative prior distributions for Bayesian estimation and identification is presented. This approach relies on constructing an optimal mixture distribution and applies in small sample sizes. The procedure is illustrated in a small-scale numerical study and contrasted with an approach based on maximum entropy
Keywords :
Bayes methods; estimation theory; identification; information theory; Bayesian estimation; Shannon information theory; finite samples; identification; least-informative prior distributions; maximum entropy; optimal mixture distribution; Automatic control; Bayesian methods; Data analysis; Entropy; Information analysis; Information theory; Laboratories; Physics; Probability distribution; Research and development;
Conference_Titel :
Decision and Control, 1989., Proceedings of the 28th IEEE Conference on
Conference_Location :
Tampa, FL
DOI :
10.1109/CDC.1989.70640
