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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 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
- Sales Rank: #4225241 in Books
- Published on: 1998-10-20
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x 1.14" w x 6.10" l, 1.60 pounds
- Binding: Hardcover
- 408 pages
Review
"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
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