Processing Consistency in Non-Bayesian Inference

43 Pages Posted: 19 Dec 2014 Last revised: 13 Mar 2017

See all articles by Xue Dong He

Xue Dong He

The Chinese University of Hong Kong - Department of Systems Engineering and Engineering Management

Di Xiao

Columbia University - Fu Foundation School of Engineering and Applied Science

Date Written: March 12, 2017

Abstract

We propose a coherent inference model that is obtained by distorting the prior density in Bayes' rule and replacing the likelihood with a so-called pseudo-likelihood. This model includes the existing non-Bayesian inference models as special cases and implies new models of base-rate neglect and conservatism. We prove a sufficient and necessary condition under which the coherent inference model is processing consistent, i.e., implies the same posterior density however the samples are grouped and processed retrospectively. We further show that processing consistency does not imply Bayes' rule by proving a sufficient and necessary condition under which the coherent inference model can be obtained by applying Bayes' rule to a false stochastic model.

Keywords: Non-Bayesian inference, processing consistency, distortion, pseudo-likelihood, false-Bayesian models, conservatism and base-rate neglect

JEL Classification: D03, D83, G02

Suggested Citation

He, Xue Dong and Xiao, Di, Processing Consistency in Non-Bayesian Inference (March 12, 2017). Available at SSRN: https://ssrn.com/abstract=2539849 or http://dx.doi.org/10.2139/ssrn.2539849

Xue Dong He (Contact Author)

The Chinese University of Hong Kong - Department of Systems Engineering and Engineering Management ( email )

505 William M.W. Mong Engineering Building
The Chinese University of Hong Kong, Shatin, N.T.
Hong Kong
Hong Kong

HOME PAGE: http://https://sites.google.com/site/xuedonghepage/home

Di Xiao

Columbia University - Fu Foundation School of Engineering and Applied Science ( email )

New York, NY
United States

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