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

chapter Chapter 2|42 pages

Spatial sampling and censoring

chapter Chapter 3|62 pages

Likelihood inference for spatial point processes

chapter Chapter 7|47 pages

Random closed sets: results and problems

chapter Chapter 8|32 pages

General shape and registration analysis