Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studies and, online, a c-library for fast and exact simulation. With chapters contributed by leading researchers in the field, this volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of science fields where spatial data analysis is important
In the analysis of spatial phenomena closely related to the local context, the probabilistic model ...
Gaussian random fields (GRFs) constitute an important part of spatial modelling, but can be computat...
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistic...
A powerful modelling tool for spatial data is the framework of Gaussian Markov random fields (GMRFs)...
Gaussian Markov random fields (GMRFs) are frequently used as computationally efficient models in spa...
AbstractGaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two dist...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
Gaussian Markov random fields (GMRF) are important families of distributions for the modeling of spa...
Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical ...
Summary. Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial stat...
AbstractGaussian Markov random fields (GMRF) are important families of distributions for the modelin...
Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial statistical m...
This paper describes the modelling and fitting of Gaussian Markov random field spatial components wi...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
This book is characterized by extremely rich content and presents in a clear and simple way both cla...
In the analysis of spatial phenomena closely related to the local context, the probabilistic model ...
Gaussian random fields (GRFs) constitute an important part of spatial modelling, but can be computat...
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistic...
A powerful modelling tool for spatial data is the framework of Gaussian Markov random fields (GMRFs)...
Gaussian Markov random fields (GMRFs) are frequently used as computationally efficient models in spa...
AbstractGaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two dist...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
Gaussian Markov random fields (GMRF) are important families of distributions for the modeling of spa...
Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical ...
Summary. Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial stat...
AbstractGaussian Markov random fields (GMRF) are important families of distributions for the modelin...
Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial statistical m...
This paper describes the modelling and fitting of Gaussian Markov random field spatial components wi...
Spatial statistics are useful in subjects as diverse as climatology, ecology, economics, environment...
This book is characterized by extremely rich content and presents in a clear and simple way both cla...
In the analysis of spatial phenomena closely related to the local context, the probabilistic model ...
Gaussian random fields (GRFs) constitute an important part of spatial modelling, but can be computat...
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistic...