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Durham University

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Publication details

Tsiftsi, Thomai, Jermyn, Ian & Einbeck, Jochen (2014), Bayesian shape modelling of cross-sectional geological data, in Kneib, Thomas, Sobotka, Fabian, Fahrenholz, Jan & Irmer, Henriette eds, II: 29th International Workshop on Statistical Modelling. Göttingen, University of Göttingen, Göttingen, 161-164.

Author(s) from Durham


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.