# Bayesian regression of piecewise homogeneous Poisson processes

## TL;DR

This paper introduces a Bayesian approach for piecewise regression tailored to Poisson process data, enabling detection of change points in count rate time series, with implementation in Mathematica tested on simulated data.

## Contribution

It adapts Bayesian piecewise regression specifically for Poisson processes and provides a computational tool for identifying change points in count data.

## Key findings

- Effective detection of change points in simulated Poisson data
- Implementation in Mathematica demonstrates practical usability
- Method shows promise for analyzing real-world count time series

## Abstract

In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes.