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.

Bayesian estimation of the discrepancy with misspecified parametric models

DE BLASI, Pierpaolo;
2013

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.
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https://bayesian.org/BA
Asymptotics; Bayesian nonparametrics; Semi-parametric density model; Gaussian process; Kullback-Leibler divergence; Posterior consistency
De Blasi P; Walker SG
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/140549
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