The simulation of random spatial data on a computer is an important tool for understanding the behavior of spatial processes. In this chapter we describe 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
Spatial processes are mathematical models for spatial data; that is, spatially ar-ranged measurement...
For inferential analysis of spatial data, probability modelling in the form of a spatial stochastic ...
Perfect simulations are methods, which convert suitable Markov chain Monte Carlo (MCMC) algorithms i...
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistic...
Many models for the study of point-referenced data explicitly introduce spatial random effects to ca...
this paper, we will describe a convenient method for generating these random processes and fields e#...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
We summarize and discuss the current state of spatial point process theory and directions for future...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very activ...
This work presents a review of some of the schemes used to perfect sample from spatial processes
I consider the use of Markov random fields (MRFs) on a fine grid to represent latent spatial process...
This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and ...
Markov point processes provide flexible models to describe interaction behavior amongst points, incl...
This chapter gives a brief introduction to spatial point processes, with a view to applications. The...
Spatial processes are mathematical models for spatial data; that is, spatially ar-ranged measurement...
For inferential analysis of spatial data, probability modelling in the form of a spatial stochastic ...
Perfect simulations are methods, which convert suitable Markov chain Monte Carlo (MCMC) algorithms i...
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistic...
Many models for the study of point-referenced data explicitly introduce spatial random effects to ca...
this paper, we will describe a convenient method for generating these random processes and fields e#...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
We summarize and discuss the current state of spatial point process theory and directions for future...
The area-interaction process and the continuum random-cluster model are characterized in terms of ce...
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very activ...
This work presents a review of some of the schemes used to perfect sample from spatial processes
I consider the use of Markov random fields (MRFs) on a fine grid to represent latent spatial process...
This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and ...
Markov point processes provide flexible models to describe interaction behavior amongst points, incl...
This chapter gives a brief introduction to spatial point processes, with a view to applications. The...
Spatial processes are mathematical models for spatial data; that is, spatially ar-ranged measurement...
For inferential analysis of spatial data, probability modelling in the form of a spatial stochastic ...
Perfect simulations are methods, which convert suitable Markov chain Monte Carlo (MCMC) algorithms i...
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