Bayesian Inference of the Stochastic Gompertz Growth Model for Tumor Growth

초록

A stochastic Gompertz diffusion model for tumor growth is a topic of active interest as cancer is a leading cause of death in Korea. The direct maximum likelihood estimation of stochastic differential equations would be possible based on the continuous path likelihood on condition that a continuous sample path of the process is recorded over the interval. This likelihood is useful in providing a basis for the so-called continuous record or infill likelihood function and infill asymptotic. In practice, we do not have fully continuous data except a few special cases. As a result, the exact ML method is not applicable. In this paper we proposed a method of parameter estimation of stochastic Gompertz differential equation via Markov chain Monte Carlo methods that is applicable for several data structures. We compared a Markov transition data structure with a data structure that have an initial point.

키워드

• Stochastic diffusion;
• Gompertz growth model;
• tumor growth;
• Bayesian;
• Markov data structure;
• sparse data structure;

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과제정보

연구 과제 주관 기관 : Chonnam National University

참고문헌

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