Modeling material stress using integrated Gaussian Markov random fields
چکیده
چکیدهای برای این سند ثبت نشده است.
اسناد مشابه
Transformed Gaussian Markov Random Fields and 1 Spatial Modeling
15 The Gaussian random field (GRF) and the Gaussian Markov random field (GMRF) have 16 been widely used to accommodate spatial dependence under the generalized linear mixed 17 model framework. These models have limitations rooted in the symmetry and thin tail of the 18 Gaussian d
Gaussian Markov Random Field Models With Applications in Spatial Statistics
Gaussian Markov Random Field (GMRF) models are frequently used in statistics, and in spatial statistics in particular. The analytical properties of the Gaussian distribution are convenient and the Markov property invaluable when constructing single site Markov chain Markov Carlo
Efficient Computations for Gaussian Markov Random Field Models with two Applications in Spatial Epidemiology
Gaussian Markov random fields (GMRFs) are frequently used in statistics, and in spatial statistics in particular. The analytical properties of the Gaussian distribution are convenient and the Markov property invaluable when constructing single site Markov chain Monte Carlo algori
NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Modelling spatial variation in disease risk using Gaussian Markov random field proxies for Gaussian random fields
Analyses of spatial variation in disease risk based on area-level summaries of disease counts are most often based on the assumption that the relative risk is uniform across each region. Such approaches introduce an artificial piecewise-constant relative risk-surface with discont
NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Gaussian Markov Random Field Models With Applications in Spatial Statistics
Gaussian Markov Random Field (GMRF) models are frequently used in statistics, and in spatial statistics in particular. The analytical properties of the Gaussian distribution are convenient and the Markov property invaluable when constructing single site Markov chain Markov Carlo
A comparative study of Gaussian geostatistical and Gaussian Markov random field models
Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two distinct approaches commonly used in modeling point referenced and areal data, respectively. In this work the relations between GMRFs and GGMs are explored based on approximations of GMRFs by