Bayesian shape modelling of cross-sectional geological data
Methodology
2018-02-28 v1 Applications
Machine Learning
Abstract
Shape information is of great importance in many applications. For example, the oil-bearing capacity of sand bodies, the subterranean remnants of ancient rivers, is related to their cross-sectional shapes. The analysis of these shapes is therefore of some interest, but current classifications are simplistic and ad hoc. In this paper, we describe the first steps towards a coherent statistical analysis of these shapes by deriving the integrated likelihood for data shapes given class parameters. The result is of interest beyond this particular application.
Cite
@article{arxiv.1802.09631,
title = {Bayesian shape modelling of cross-sectional geological data},
author = {Thomai Tsiftsi and Ian H. Jermyn and Jochen Einbeck},
journal= {arXiv preprint arXiv:1802.09631},
year = {2018}
}
Comments
4 pages, 1 figure, In proceedings 29th International Workshop on Statistical Modelling, 14-18 July 2014, Gottingen, Germany. Amsterdam: Statistical Modelling Society, pp. 161-164