An inverse of Sanov´s theorem

Author

Ganesh, A.J. ; O´Connell, Neil

Author_Institution

BRIMS, Hewlett-Packard Labs., Bristol, UK

fYear

1998

fDate

16-21 Aug 1998

Firstpage

413

Abstract

Let (X_k)_k⩾1 be a sequence of independent, identically distributed random variables taking values in a finite set, and consider the problem of estimating the law of X₁ in a Bayesian framework. We prove that the sequence of posterior distributions satisfies a large deviation principle, and give an explicit expression for the rate function. We extend the result to Bayesian inference on compact metric spaces using Dirichlet process priors

Keywords

Bayes methods; inverse problems; random processes; sequences; Bayesian inference; Dirichlet process priors; Sanov´s theorem; compact metric spaces; finite set; identically distributed random variables; inverse inference; large deviation principle; law; posterior distributions; rate function; sequence; Bayesian methods; Entropy; Extraterrestrial measurements; Probability; Random sequences; Random variables; Statistical distributions; Topology;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 1998. Proceedings. 1998 IEEE International Symposium on

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-5000-6

Type

conf

DOI

10.1109/ISIT.1998.709018

Filename

709018

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2493834