A Bayesian sequential soft classification problem for a Brownian motion's drift

Fuente: arXiv
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Autori principali: Campbell, Steven, Zhang, Yuchong
Natura: Preprint
Pubblicazione: 2025
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author Campbell, Steven
Zhang, Yuchong
author_facet Campbell, Steven
Zhang, Yuchong
contents In this note we introduce and solve a soft classification version of the famous Bayesian sequential testing problem for a Brownian motion's drift. We establish that the value function is the unique non-trivial solution to a free boundary problem, and that the continuation region is characterized by two boundaries which may coincide if the observed signal is not strong enough. By exploiting the solution structure we are able to characterize the functional dependence of the stopping boundaries on the signal-to-noise ratio. We illustrate this relationship and compare our stopping boundaries to those derived in the classical setting.
format Preprint
id arxiv_https___arxiv_org_abs_2501_11314
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Bayesian sequential soft classification problem for a Brownian motion's drift
Campbell, Steven
Zhang, Yuchong
Probability
Statistics Theory
60G35, 60G40, 62L10, 62L15
In this note we introduce and solve a soft classification version of the famous Bayesian sequential testing problem for a Brownian motion's drift. We establish that the value function is the unique non-trivial solution to a free boundary problem, and that the continuation region is characterized by two boundaries which may coincide if the observed signal is not strong enough. By exploiting the solution structure we are able to characterize the functional dependence of the stopping boundaries on the signal-to-noise ratio. We illustrate this relationship and compare our stopping boundaries to those derived in the classical setting.
title A Bayesian sequential soft classification problem for a Brownian motion's drift
topic Probability
Statistics Theory
60G35, 60G40, 62L10, 62L15
url https://arxiv.org/abs/2501.11314