Stochastic geometry likelihood and computation : [papers given at the Third Séminaire Européen de Statistique on 'Stochastic Geometry, Theory and Applications', held at Université Paul Sabatier, Toulouse, 13-18 May 1996

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 Stochastic geometry concerns 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 will provide lasting value as basic reference, equally valuable to young researchers, to established research scientists in the area, and the increasing number of mathematical scientists working other areas who research needs are now driving the development of stochastic geometry This chapter aims to give the reader a rapid introduction to the main ideas of stochastic geometry.