1st Edition

Stochastic Geometry Likelihood and Computation

Edited By Wilfrid S. Kendall, M.N.M. van Lieshout Copyright 1998
    418 Pages
    by Chapman & Hall

    Stochastic geometry involves the study of random geometric structures, and blends geometric, probabilistic, and statistical methods to provide powerful techniques for modeling and analysis. Recent developments in computational statistical analysis, particularly Markov chain Monte Carlo, have enormously extended the range of feasible applications. Stochastic Geometry: Likelihood and Computation provides a coordinated collection of chapters on important aspects of the rapidly developing field of stochastic geometry, including:
    o a "crash-course" introduction to key stochastic geometry themes
    o considerations of geometric sampling bias issues
    o tesselations
    o shape
    o random sets
    o image analysis
    o spectacular advances in likelihood-based inference now available to stochastic geometry through the techniques of Markov chain Monte Carlo

    Crash Course in Stochastic Geometry
    Sampling and Censoring
    Likelihood Inference for Spatial Point Processes
    Markov Chain Monte Carlo and Spatial Point Processes
    Topics in Voronoi and Johnson-Mehl Tessellations
    Mathematical Morphology
    Random Closed Sets
    General Shape and Registration Analysis
    Nash Inequalities


    O.E. Barndorff-Nielsen Professor of Theoretical Statistics Institute of Mathematics Aarhus Denmark. W.S. Kendall Professor of Statistics University of Warwick UK and M.N.M. van Lieshout Centre for Science and Information (CWI) Amsterdam The Netherlands

    "This useful collection of papers highlights various aspects of modern stochastic geometry. The papers included here provide a rare opportunity to grasp new major trends in stochastic geometry and related areas."
    -Mathematical Reviews