A Bayesian Hierarchical Nonoverlapping Random Disc Growth Model

• 成果类型: Article
• 署名作者: Micheas, Athanasios C.; Wikle, Christopher K.
• 署名单位: University of Missouri System; University of Missouri Columbia
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0124
• 发表日期: 2009
• 页码: 274-283
• 关键词: markov point-processes; precipitation; distributions; verification; sydney-2000

摘要

A methodology is proposed to efficiently model a random set via a multistage hierarchical Bayesian model. We define a NonOverlapping Random Disk Model (NORDM), which is similar in spirit to the well-known Poisson-Boolean model. This model is formulated in a conditional setting that facilitates Bayesian sampling of important parameters in the model. This framework can accommodate any object, not just those with disk shapes, although the model can be easily extended to include any known compact convex set instead of the disc (e.g., polygons or ellipses). We further propose a growth model that is conceptually simple and allows straightforward estimation of parameters, without the need for tedious calculations of hitting or inclusion probabilities. The model is applied to severe storm cell development as obtained from weather radar.