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Sensitivity Analyses for Tumor Growth Models

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.

Identiferoai:union.ndltd.org:WKU/oai:digitalcommons.wku.edu:theses-4122
Date01 April 2019
CreatorsMendis, Ruchini Dilinika
PublisherTopSCHOLAR®
Source SetsWestern Kentucky University Theses
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceMasters Theses & Specialist Projects

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