具体描述
This book demonstrates the usefulness of geometric methods in probability theory and mathematical statistics, and shows close relationships between these disciplines and convex analysis. Deep facts and statements from the theory of convex sets are discussed with their applications to various questions arising in probability theory, mathematical statistics, and the theory of stochastic processes. The book is essentially self-contained, and the presentation of material is thorough in detail. Audience: The topics considered in the book are accessible to a wide audience of mathematicians, and graduate and postgraduate students, whose interests lie in probability theory and convex geometry.
作者简介
目录信息
Convex sets in vector spaces....Pages 1-18
Brunn-Minkowski inequality....Pages 19-36
Convex polyhedra....Pages 37-48
Two classical isoperimetric problems....Pages 49-56
Some infinite-dimensional vector spaces....Pages 57-70
Probability measures and random elements....Pages 71-94
Convergence of random elements....Pages 95-106
The structure of supports of Borel measures....Pages 107-122
Quasi-invariant probability measures....Pages 123-144
Anderson inequality and unimodal distributions....Pages 145-172
Oscillation phenomena and extensions of measures....Pages 173-196
Comparison principles for Gaussian processes....Pages 197-220
Integration of vector-valued functions and optimal estimation of stochastic processes....Pages 221-254
Back Matter....Pages 255-303
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