Article ID Journal Published Year Pages File Type
1147412 Journal of Statistical Planning and Inference 2014 20 Pages PDF
Abstract
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.
Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
Authors
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