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Activity Number: 250
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304911
Title: Random Set Models for Growth with Application to Nowcasting
Author(s): Rima Dey*+ and Athanasios Micheas
Companies: University of Missouri-Columbia and University of Missouri-Columbia
Address: 1205 University Ave, Columbia, MO, 65201, United States
Keywords: Random Set ; Hierarchical Bayesian Model ; Hereditary Growth Model ; Finite Mixture Model ; Data Augmentation ; Markov Chain Monte Carlo

Growth or evolution of objects in nature is ubiquitous. It can be modeled in number of different ways. Our main objective here is to develop a model to capture growth of objects over time. For this purpose, we propose a methodology to model random sets that describe the objects using hierarchical Bayesian framework. This work is an extension of the random disc growth model proposed by Micheas and Wikle (2009). Herein, instead of random discs, we generalize the methodology by defining non-overlapping random convex polygons, similar in essence to the Poisson-Boolean model. We consider three types of growth models, hereditary, birth-death and mixed model. We illustrate the formulation to hereditary type of growth models. Estimation is carried out via Markov Chain Monte Carlo (MCMC). The methodology is exemplified with an application to severe weather precipitation fields as obtained from weather radar images.

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