Date of Award

Fall 2022

Document Type


Degree Name

Master of Science (MS) in Mathematics


Mathematical, Computing & Information Sciences

Committee Chair

Dr. Jason Cleveland


In this paper we explore Bayesian inference and its application to the problem of estimating the intensity function of a non-homogeneous Poisson process. These processes model the behavior of phenomena in which one or more events, known as arrivals, occur independently of one another over a certain period of time. We are concerned with the number of events occurring during particular time intervals across several realizations of the process. We show that given sufficient data, we are able to construct a piecewise-constant function which accurately estimates the mean rates on particular intervals. Further, we show that as we reduce these intervals in size, at the limit we are able to reconstruct the original intensity function.



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