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

chapter Chapter 1|35 pages

A crash course in stochastic geometry

ByAdrian J. Baddeley

chapter Chapter 2|42 pages

Spatial sampling and censoring

ByAdrian J. Baddeley

chapter Chapter 3|62 pages

Likelihood inference for spatial point processes

ByC. Geyer

chapter Chapter 4|32 pages

Markov chain Monte Carlo and spatial point processes

ByJ. Møller

chapter Chapter 5|26 pages

Topics in Voronoi and Johnson—Mehl tessellations

ByJ. Møller

chapter Chapter 6|85 pages

Current Topics in Applied Morphological Image Analysis

ByLuc Vincent

chapter Chapter 7|47 pages

Random closed sets: results and problems

ByIlya Molchanov

chapter Chapter 8|32 pages

General shape and registration analysis

ByIan Dryden

chapter Chapter 9|36 pages

Simple examples of the use of Nash inequalities for finite Markov chains

ByL. Saloff-Coste