# The Relation Between Bayesian Fisher Information and Shannon Information for Detecting a Change in a Parameter

## TL;DR

This paper establishes a theoretical connection between Shannon Information, Fisher Information, and Bayesian Fisher Information in the context of detecting changes in a parameter, linking estimation and detection performance measures.

## Contribution

It introduces a novel theoretical framework linking Shannon Information and Fisher Information through Bayesian Fisher Information for detection tasks.

## Key findings

- Derived a relationship between Shannon Information and Fisher Information.
- Connected Bayesian Fisher Information to detection performance metrics.
- Outlined a cycle of relations involving Minimum Probability of Error and Mean Squared Error.

## Abstract

We derive a connection between performance of estimators the performance of the ideal observer on related detection tasks. Specifically we show how Shannon Information for the task of detecting a change in a parameter is related to the Fisher Information and the Bayesian Fisher Information. We have previously shown that this Shannon Information is related via an integral transform to the Minimum Probability of Error on the same task. We then outline a circle of relations starting with this Minimum Probability of Error and Ensemble Mean Squared Error via the Ziv-Zakai inequality, then the Ensemble Mean Squared error for an estimator and the Bayesian Fisher Information via the van Trees Inequality, and finally the Bayesian Fisher Information and the Shannon Information for a detection task via the work here.