Title :
Bayesian estimation, large deviations, and incomplete data
Author_Institution :
Inst. of Inf. Theory & Autom., Acad. of Sci., Prague, Czech Republic
Abstract :
The paper suggests an approximation of Bayesian parameter estimation for the case that data are incomplete. Attractive properties of the approximation follow from the large deviation theorem and the elementary properties of the informational divergence
Keywords :
Bayes methods; parameter estimation; Bayesian parameter estimation; approximation; incomplete data; informational divergence; large deviations; Aggregates; Automation; Bayesian methods; Data engineering; Econometrics; Information theory; Parameter estimation; Random variables; Recursive estimation; Statistics;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.410861
