Measuring and Controlling Bias for Some Bayesian Inferences and the Relation to Frequentist Criteria

• Main Authors: Michael Evans, Yang Guo
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Entropy
• Subjects: principle of evidence; bias against; bias in favor; plausible region; frequentism; confidence
• Online Access: https://www.mdpi.com/1099-4300/23/2/190

A common concern with Bayesian methodology in scientific contexts is that inferences can be heavily influenced by subjective biases. As presented here, there are two types of bias for some quantity of interest: bias against and bias in favor. Based upon the principle of evidence, it is shown how to measure and control these biases for both hypothesis assessment and estimation problems. Optimality results are established for the principle of evidence as the basis of the approach to these problems. A close relationship is established between measuring bias in Bayesian inferences and frequentist properties that hold for any proper prior. This leads to a possible resolution to an apparent conflict between these approaches to statistical reasoning. Frequentism is seen as establishing figures of merit for a statistical study, while Bayes determines the inferences based upon statistical evidence.