Hierarchical probability models and Bayesian analysis of mine locations

被引：13
作者：
Cressie, N
Lawson, AB
机构：
[1] Ohio State Univ, Dept Stat, Columbus, OH 43210 USA
[2] Univ Aberdeen, Dept Math Sci, Aberdeen, Scotland
来源：
ADVANCES IN APPLIED PROBABILITY | 2000年 / 32卷 / 02期
关键词：
Cox point process; Markov chain Monte Carlo; Poisson point process; posterior distribution; prior distribution;
D O I：
10.1239/aap/1013540165
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Based on remote sensing of a potential minefield, point locations are identified, some of which may not be mines. The mines and mine-like objects are to be distinguished based on their point patterns, although it must be emphasized that all one sees is the superposition of their locations. In this paper, we construct a hierarchical spatial point-process model that accounts for the different patterns of mines and mine-like objects and uses posterior analysis to distinguish between them. Our Bayesian approach is applied to minefield data obtained from a multispectral video remote-sensing system.
引用
收藏
页码：315 / 330
页数：16
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