Bayesian spatio-temporal prediction of cancer dynamics
In this paper we analyze the spatio-temporal dynamics of brain tumors. These objects are originally processed from computer tomography images, and can be depicted as a collection of image pixels with varying degrees of color intensity levels. We consider spatio-temporal stochastic processes within a Bayesian framework to model spatial heterogeneity, temporal dependence and spatio-temporal interactions amongst the pixels, providing a general modeling framework for such dynamics. We aim at predicting cancer growth in space and time. We analyze real data on brain tumor based on a set of images taken in several time lags.
bayesian inference gaussian random field integrated nested laplace approximations spacetime modeling stochastic partial differential equations tumor growth