Stochastic Geometry, Spatial Statistics and Random Fields Models and Algorithms / [electronic resource] :
edited by Volker Schmidt.
- 1st ed. 2015.
- XXIV, 464 p. 133 illus., 63 illus. in color. online resource.
- Lecture Notes in Mathematics, 2120 0075-8434 ; .
- Lecture Notes in Mathematics, 2120 .
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.
9783319100647
10.1007/978-3-319-10064-7 doi
Probabilities.
Mathematical models.
Algorithms.
Geometry.
Probability Theory and Stochastic Processes.
Mathematical Modeling and Industrial Mathematics.
Algorithms.
Geometry.
QA273.A1-274.9 QA274-274.9
519.2
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.
9783319100647
10.1007/978-3-319-10064-7 doi
Probabilities.
Mathematical models.
Algorithms.
Geometry.
Probability Theory and Stochastic Processes.
Mathematical Modeling and Industrial Mathematics.
Algorithms.
Geometry.
QA273.A1-274.9 QA274-274.9
519.2