Modeling the growth of objects through a stochastic process of random sets

We develop models to capture the growth or evolution of objects over time as well as provide forecasts to describe the object in future states utilizing information from the current state. For this purpose, we propose a methodology to model random sets (RS) that describe the objects using a hierarchical Bayesian framework. Estimation of the model parameters is carried out using Markov Chain Monte Carlo (MCMC). The methodology is exemplified with an application to nowcasting of severe weather precipitation fields as obtained from weather radar images, where severe storm cells are treated as random sets.