# Exponential Change of Measure for General Piecewise Deterministic Markov Processes

## TL;DR

This paper introduces a new exponential change of measure for general PDMPs with measure-valued generators, allowing the process to be analyzed under a transformed probability measure while preserving its PDMP properties.

## Contribution

It develops a general form of exponential martingales for PDMPs with non-absolutely continuous inter-occurrence times and constructs a new measure under which the process remains a PDMP.

## Key findings

- Derived a new exponential martingale for PDMPs.
- Defined a new probability measure preserving the PDMP structure.
- Identified the measure-valued generator under the new measure.

## Abstract

We consider a general piecewise deterministic Markov process (PDMP) $X=\{X_t\}_{t\geqslant 0}$ with measure-valued generator $\mathcal{A}$, for which the conditional distribution function of the inter-occurrence time is not necessarily absolutely continuous. A general form of the exponential martingales is presented as $$M^f_t=\frac{f(X_t)}{f(X_0)}\left[\mathrm{Sexp}\left(\int_{(0,t]}\frac{\mathrm{d}L(\mathcal{A}f)_s}{f(X_{s-})}\right)\right]^{-1}.$$ Using this exponential martingale as a likelihood ratio process, we define a new probability measure. It is shown that the original process remains a general PDMP under the new probability measure. And we find the new measure-valued generator and its domain.