Stochastic Geometry Spatial Statistics and Random Fields : Models and Algorithms Stein’s Method for Approximating Complex Distributions with a View towards Point Processes Clustering Comparison of Point Processes with Applications to Random Geometric Models Random Tessellations and their Application to the Modelling of Cellular Materials Stochastic 3D Models for the Micro structure of Advanced Functional Materials Boolean Random Functions Random Marked Sets and Dimension Reduction Space Time Models in Stochastic Geometry Rotational Integral Geometry and Local Stereology with a View to Image Analysis An Introduction to Functional Data Analysis Some Statistical Methods in Genetics Extrapolation of Stationary Random Fields Spatial Process Simulation Introduction to Coupling from the Past using R References Index 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