期刊
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
卷 143, 期 10, 页码 1621-1633出版社
ELSEVIER
DOI: 10.1016/j.jspi.2013.05.013
关键词
Bayesian asymptotics; Exchangeable; Independent and identically distributed; Learning model; Misspecified model; Mixture of Dirichlet process model; Regression model; Time series model
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.
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