Titlebook: Stochastic Geometry, Spatial Statistics and Random Fields; Models and Algorithm Volker Schmidt Book 2015 Springer International Publishing

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Space-Time Models in Stochastic Geometry,tion are growth models of random sets. The present chapter aims to present more general models. It has two parts according to whether the time is considered to be discrete or continuous. In the discrete-time case we focus on state-space models and the use of Monte Carlo methods for the inference of

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Rotational Integral Geometry and Local Stereology - with a View to Image Analysis,ve properties of spatial structures. In rotational integral geometry, focus is on integrals of geometric functionals with respect to rotation invariant measures. Rotational integrals of intrinsic volumes are studied. The opposite problem of expressing intrinsic volumes as rotational integrals is als

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Some Statistical Methods in Genetics,he progress in the human genome reading permitted to collect the genetic datasets for analysis by means of various complementary statistical tools. The intensive studies in this research domain are carried out in leading research centers all over the world. One has to operate with data of huge dimen

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Extrapolation of Stationary Random Fields,ion techniques. For (non–Gaussian) stable fields, which are known to be heavy-tailed, we describe further extrapolation methods and discuss their properties. Two of them can be seen as direct generalizations of kriging.

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Spatial Process Simulation,ibe how to simulate realizations from the main types of spatial processes, including Gaussian and Markov random fields, point processes, spatial Wiener processes, and Lévy fields. Concrete MATLAB code is provided.

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Introduction to Coupling-from-the-Past using ,,h can be run in the statistical scripting language .. The simple examples are: symmetric random walk with two reflecting boundaries, a very basic continuous state-space Markov chain, the Ising model with external field, and random walk with negative drift and a reflecting boundary at the origin. In

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https://doi.org/10.1007/978-3-319-10064-760D05,60G55,60G60,62H11,62M40,62P30,62P10; ; Mathematical Models; Random Fields; Simulation Algorithms; S

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