Integral Geometry – Measure Theoretic Approach and Stochastic Applications
Integral geometry – measure theoretic approach and stochastic applications Rolf Schneider Preface Integral geometry, as it is understood here, deals with the computation and application of geometric mean values with respect to invariant measures. In the following, I want to give an introduction to the integral geometry of polyconvex sets (i.e., finite unions of compact convex sets) in Euclidean spaces. The invariant or Haar measures that occur will therefore be those on the groups of translations, rotations, or rigid motions of Euclidean space, and on the affine Grassmannians of k-dimensional affine subspaces. How- ever, it is also in a different sense that invariant measures will play a central role, namely in the form of finitely additive functions on polyconvex sets. Such functions have been called additive functionals or valuations in the literature, and their motion invariant specializations, now called intrinsic volumes, have played an essential role in Hadwiger’s [2] and later work (e.g., [8]) on integral geometry. More recently, the importance of these functionals for integral geometry has been rediscovered by Rota [5] and Klain-Rota [4], who called them ‘measures’ and emphasized their role in certain parts of geometric probability. We will, more generally, deal with local versions of the intrinsic volumes, the curvature measures, and derive integral-geometric results for them. This is the third aspect of the measure theoretic approach mentioned in the title. A particular feature of this approach is the essential role that uniqueness results for invariant measures play in the proofs. As prerequisites, we assume some familiarity with basic facts from mea- sure and integration theory.
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