Document Type
Thesis
Date of Award
5-31-2024
Degree Name
Master of Science in Applied Statistics - (M.S.)
Department
Mathematical Sciences
First Advisor
Casey Diekman
Second Advisor
Ji Meng Loh
Third Advisor
Victor Victorovich Matveev
Fourth Advisor
Chong Jin
Abstract
Given a parametric family of models and observational data, a researcher may be faced with an inverse problem: what distribution of parameters best creates a set of models that produce the observed data? Traditionally, Markov Chain Monte Carlo (MCMC) has commonly been used as a method to solve these stochastic inverse problems. In recent years, however, Generative Adversarial Networks (GANs) have been employed. The effectiveness of Markov Chain Monte Carlo methods as compared to a conditional generative adversarial network (cGAN) when applied to a family of models produced by a system of ordinary differential equations that model viral load over time in individuals with SARS-CoV-2 is explored.
Recommended Citation
Epstein, Elizabeth, "Comparison of methods for creating populations of models by solving stochastic inverse problems" (2024). Theses. 2585.
https://digitalcommons.njit.edu/theses/2585
