Title: Bayesian shape modelling of cross-sectional geological data,
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.