Some Remarks on Bayesian Multiple Hypothesis Testing

TL;DR

This paper develops a type-based analysis for Bayesian multiple hypothesis testing, extending classical error exponent results to cases with uncertain or estimated distributions, and proposes a robust testing method.

Contribution

It introduces a novel type-based framework to analyze error exponents under distribution uncertainty and derives a robust test for close nominal distributions.

Findings

Explicit calculation of error exponents for different distribution types

Extension of classical analysis to uncertain distribution scenarios

Development of a robust hypothesis testing method

Abstract

We consider Bayesian multiple hypothesis problem with independent and identically distributed observations. The classical, Sanov's theorem-based, analysis of the error probability allows one to characterize the best achievable error exponent. However, this analysis does not generalize to the case where the true distributions of the hypothesis are not exact or partially known via some nominal distributions. This problem has practical significance, because the nominal distributions may be quantized versions of the true distributions in a hardware implementation, or they may be estimates of the true distributions obtained from labeled training sequences as in statistical classification. In this paper, we develop a type-based analysis to investigate Bayesian multiple hypothesis testing problem. Our analysis allows one to explicitly calculate the error exponent of a given type and extends the classical analysis. As a generalization of the proposed method, we derive a robust test and obtain its error exponent for the case where the hypothesis distributions are not known but there exist nominal distribution that are close to true distributions in variational distance.