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.Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015)This paper, by D J R Sevilla, is licensed under the Creative Commons Attribution License 3.0.
Highlights
Bayesian statistics have revolutionized data analysis [1]
The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes
As the members of the Poisson distributions family are identified by one parameter -the mean rate r of the Poisson process, the PDF of the models
Summary
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
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