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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 themeso considerations of geometric sampling bias issueso tesselationso shapeo random setso image analysiso spectacular advances in likelihood-based inference now available to stochastic geometry through the techniques of Markov chain Monte Carlo
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
Preface. Recent Developments in Stochastic Geometry and Stereology. Markov Chain Monte Carlo Techniques. Markov Chain Monte Carlo and Spatial Point Process. Topics in Voronoi and Johnson-Mehl Tessellations. Mathematical Morphology. Rates of Convergence of Markov Chains. Shape Theory. Random Sets: Results, Problems and Perspectives.
"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