Objective Bayesian analysis under sequential experimentation

TL;DR

This paper explores the development of objective Bayesian priors tailored for sequential experiments, addressing how stopping rules influence prior selection and providing new formulas and computational methods.

Contribution

It introduces novel expressions for reference priors in sequential settings and discusses computational challenges, advancing objective Bayesian analysis in sequential experimentation.

Findings

Derived new formulas for reference priors under various sequential contexts

Highlighted the dependence of common priors on stopping rules

Addressed computational issues in implementing these priors

Abstract

Objective priors for sequential experiments are considered. Common priors, such as the Jeffreys prior and the reference prior, will typically depend on the stopping rule used for the sequential experiment. New expressions for reference priors are obtained in various contexts, and computational issues involving such priors are considered.