Institute of Mathematical Statistics Lecture Notes - Monograph Series

Evaluating improper priors and the recurrence of symmetric Markov chains: an overview

Morris L. Eaton

Full-text: Open access

Abstract

Given a parametric statistical model, an improper prior distribution can often be used to induce a proper posterior distribution (an inference). This inference can then be used to solve decision problems once an action space and loss have been specified. One way to evaluate the inference is to ask for which estimation problems does the above formal Bayes method produce admissible estimators. The relationship of this problem to the recurrence of an associated symmetric Markov chain is reviewed.

Chapter information

Source
Anirban DasGupta, ed., A Festschrift for Herman Rubin (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2004), 5-20

Dates
First available in Project Euclid: 28 November 2007

Permanent link to this document
https://projecteuclid.org/euclid.lnms/1196285376

Digital Object Identifier
doi:10.1214/lnms/1196285376

Mathematical Reviews number (MathSciNet)
MR2126883

Zentralblatt MATH identifier
1268.62010

Subjects
Primary: 62A01: Foundations and philosophical topics 62C15: Admissibility 62F15: Bayesian inference

Keywords
formal Bayes rules admissibility Markov chains

Rights
Copyright © 2004, Institute of Mathematical Statistics

Citation

Eaton, Morris L. Evaluating improper priors and the recurrence of symmetric Markov chains: an overview. A Festschrift for Herman Rubin, 5--20, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2004. doi:10.1214/lnms/1196285376. https://projecteuclid.org/euclid.lnms/1196285376


Export citation