Bayesian Analysis

Bayesian Estimation of the Discrepancy with Misspecified Parametric Models

Pierpaolo De Blasi and Stephen G. Walker

Full-text: Open access

Abstract

We study a Bayesian model where we have made specific requests about the parameter values to be estimated. The aim is to find the parameter of a parametric family which minimizes a distance to the data generating density and then to estimate the discrepancy using nonparametric methods. We illustrate how coherent updating can proceed given that the standard Bayesian posterior from an unidentifiable model is inappropriate. Our updating is performed using Markov Chain Monte Carlo methods and in particular a novel method for dealing with intractable normalizing constants is required. Illustrations using synthetic data are provided.

Article information

Source
Bayesian Anal., Volume 8, Number 4 (2013), 781-800.

Dates
First available in Project Euclid: 4 December 2013

Permanent link to this document
https://projecteuclid.org/euclid.ba/1386166313

Digital Object Identifier
doi:10.1214/13-BA024

Mathematical Reviews number (MathSciNet)
MR3150469

Zentralblatt MATH identifier
1329.62123

Keywords
Asymptotics Bayesian nonparametrics Semi–parametric density model Gaussian process Kullback–Leibler divergence Posterior consistency

Citation

De Blasi, Pierpaolo; Walker, Stephen G. Bayesian Estimation of the Discrepancy with Misspecified Parametric Models. Bayesian Anal. 8 (2013), no. 4, 781--800. doi:10.1214/13-BA024. https://projecteuclid.org/euclid.ba/1386166313


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