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Hierarchical probability models and Bayesian analysis of mine locations

Part of: Decision theory Stochastic processes

Published online by Cambridge University Press:  01 July 2016

Noel Cressie  and
Andrew B. Lawson
Noel Cressie*
Affiliation:
Ohio State University
Andrew B. Lawson*
Affiliation:
University of Aberdeen
*
∗ Postal address: Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus OH43210, USA.
∗∗ Postal address: Department of Mathematical Sciences, University of Aberdeen, King's College, Old Aberdeen, UK. Email address: a.lawson@maths.abdn.ac.uk
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Abstract

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.

Keywords

Cox point processMarkov chain Monte CarloPoisson point processposterior distributionprior distribution

MSC classification

Primary: 60G55: Point processes
Secondary: 62C05: General considerations

Information

Type
Stochastic Geometry and Statistical Applications
Information
Advances in Applied Probability , Volume 32 , Issue 2 , June 2000 , pp. 315 - 330
Copyright
Copyright © Applied Probability Trust 2000 

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