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Determination of Boolean models by densities of mixed volumes

Part of: Inference from stochastic processes General convexity Stochastic processes Geometric probability and stochastic geometry

Published online by Cambridge University Press:  22 July 2019

Daniel Hug  and
Wolfgang Weil
Daniel Hug*
Affiliation:
Karlsruhe Institute of Technology (KIT)
Wolfgang Weil*
Affiliation:
Karlsruhe Institute of Technology (KIT)
*
*Postal address: Department of Mathematics, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany.
*Postal address: Department of Mathematics, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany.
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Abstract

In Weil (2001) formulae were proved for stationary Boolean models Z in ℝd with convex or polyconvex grains, which express the densities (specific mean values) of mixed volumes of Z in terms of related mean values of the underlying Poisson particle process X. These formulae were then used to show that in dimensions 2 and 3 the densities of mixed volumes of Z determine the intensity γ of X. For d = 4, a corresponding result was also stated, but the proof given was incomplete, since in the formula for the density of the Euler characteristic V̅0(Z) of Z a term $\overline V^{(0)}_{2,2}(X,X)$ was missing. This was pointed out in Goodey and Weil (2002), where it was also explained that a new decomposition result for mixed volumes and mixed translative functionals would be needed to complete the proof. Such a general decomposition result has recently been proved by Hug, Rataj, and Weil (2013), (2018) and is based on flag measures of the convex bodies involved. Here, we show that such flag representations not only lead to a correct derivation of the four-dimensional result, but even yield a corresponding uniqueness theorem in all dimensions. In the proof of the latter we make use of Alesker’s representation theorem for translation invariant valuations. We also discuss which shape information can be obtained in this way and comment on the situation in the nonstationary case.

Keywords

Stationary Boolean modelintensityspecific mean valuePoisson processmixed volumemixed functionaltranslative integral geometryflag measure

MSC classification

Primary: 60D05: Geometric probability and stochastic geometry 52A22: Random convex sets and integral geometry
Secondary: 52A39: Mixed volumes and related topics 60G55: Point processes 62M30: Spatial processes
Type
Original Article
Information
Advances in Applied Probability , Volume 51 , Issue 1 , March 2019 , pp. 116 - 135
Copyright
© Applied Probability Trust 2019 

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References

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