Date of Award

May 2024

Degree Type


Degree Name

Doctor of Philosophy



First Advisor

David Spade

Committee Members

Richard Stockbridge, Istvan Lauko, Vytaras Brazauskas, Chao Zhu


ARMA, Bayesian, Change point, covid, gibbs, well log data


Changepoint detection involves the discovery of abrupt fluctuations in population dynamics over time. We take a Bayesian approach to estimating points in time at which the parameters of an autoregressive moving average (ARMA) change, applying a Markov chain Monte Carlo method. We specifically assume that data may originate from one of two groups. We provide estimates of all multi-group parameters of a model of this form for both simulated and real-world data sets. We include a provision to resolve the problem of confounding ARMA parameter estimates and variance of segment data. We apply our model to identify points in time at which influential events affecting 2020 and 2021 outbreaks of COVID-19 in Waukesha County, Wisconsin, may have occurred.