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Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.

Mathematics. --- Probability Theory and Stochastic Processes. --- Mathematical Modeling and Industrial Mathematics. --- Algorithms. --- Geometry. --- Distribution (Probability theory). --- Mathématiques --- Algorithmes --- Géométrie --- Distribution (Théorie des probabilités) --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Mathematical models. --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Euclid's Elements --- Models, Mathematical --- Simulation methods --- Algorism --- Algebra --- Arithmetic --- Math --- Science --- Foundations --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Stochastic geometry --- Spatial analysis (Statistics)

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