Optimal sequential multiple hypothesis tests

TL;DR

This paper characterizes the structure of optimal sequential tests for multiple hypotheses about a discrete-time stochastic process, considering both Bayesian and conditional frameworks, advancing the theoretical understanding of such testing procedures.

Contribution

It provides a comprehensive characterization of the structure of optimal sequential tests in multiple hypothesis testing for stochastic processes, covering Bayesian and conditional scenarios.

Findings

Characterization of optimal test structures

Applicability to Bayesian and conditional settings

Theoretical insights into sequential testing

Abstract

This work deals with a general problem of testing multiple hypotheses about the distribution of a discrete-time stochastic process. Both the Bayesian and the conditional settings are considered. The structure of optimal sequential tests is characterized.