Year of Graduation
2021
Level of Access
Open Access Thesis
Embargo Period
5-20-2021
Department or Program
Mathematics
First Advisor
Jack O'Brien, PhD
Abstract
The standard statistical methodology for analyzing complex case-control studies in ethology is often limited by approaches that force researchers to model distinct aspects of biological processes in a piecemeal, disjointed fashion. By developing a hierarchical Bayesian model, this work demonstrates that statistical inference in this context can be done using a single coherent framework. To do this, we construct a continuous-time Markov chain (CTMC) to model bumblebee foraging behavior. To connect the experimental design with the CTMC, we employ a mixture model controlled by a logistic regression on the two-factor design matrix. We then show how to infer these model parameters from experimental data using Markov chain Monte Carlo and interpret the results from a motivating experiment.
Included in
Apiculture Commons, Bioinformatics Commons, Biostatistics Commons, Data Science Commons, Entomology Commons, Statistical Methodology Commons, Statistical Models Commons