Journal
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume 143, Issue 10, Pages 1621-1633Publisher
ELSEVIER
DOI: 10.1016/j.jspi.2013.05.013
Keywords
Bayesian asymptotics; Exchangeable; Independent and identically distributed; Learning model; Misspecified model; Mixture of Dirichlet process model; Regression model; Time series model
Categories
Ask authors/readers for more resources
This article reviews Bayesian inference from the perspective that the designated model is misspecified. This misspecification has implications in interpretation of objects, such as the prior distribution, which has been the cause of recent questioning of the appropriateness of Bayesian inference in this scenario. The main focus of this article is to establish the suitability of applying the Bayes update to a misspecified model, and relies on representation theorems for sequences of symmetric distributions; the identification of parameter values of interest; and the construction of sequences of distributions which act as the guesses as to where the next observation is coming from. A conclusion is that a clear identification of the fundamental starting point for the Bayesian is described. (C) 2013 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available