Fitting dynamic growth models of biological phenomena from sample observations through Gaussian diffusion processes.

Abstract

This paper addresses the building of stochastic models that adequately describe dynamic phenomena and, in particular, those that occur in the Biosciences. In this context, the empirical fitting of a Gaussian diffusion process from sample data of a dynamic growth phenomenon is considered. In order to do this, a methodology based on approximations to its mean and variance functions is presented. Finally, several applications based on simulated and real data have been carried out.