Advisor(s) - Committee Chair
Dr. Ngoc Nguyen (Director), Dr. Ferhan Atici and Dr. Richard Schugart
Department of Mathematics
Master of Science
This study consists of the sensitivity analysis for two previously developed tumor growth models: Gompertz model and quotient model. The two models are considered in both continuous and discrete time. In continuous time, model parameters are estimated using least-square method, while in discrete time, the partial-sum method is used. Moreover, frequentist and Bayesian methods are used to construct confidence intervals and credible intervals for the model parameters. We apply the Markov Chain Monte Carlo (MCMC) techniques with the Random Walk Metropolis algorithm with Non-informative Prior and the Delayed Rejection Adoptive Metropolis (DRAM) algorithm to construct parameters' posterior distributions and then obtain credible intervals.
Mendis, Ruchini Dilinika, "Sensitivity Analyses for Tumor Growth Models" (2019). Masters Theses & Specialist Projects. Paper 3113.