1 citations · 1 across the 5 of their papers we have counts for
6 papers
The Spatial Kernel Predictor based on Huge Observation Sets
Henning Omre, Mina Spremić
Spatial prediction in an arbitrary location, based on a spatial set of observations, is usually performed by Kriging, being the best linear unbiased predictor (BLUP) in a least-squ…
Spatio-temporal Inversion using the Selection Kalman Model
Maxime Conjard, Henning Omre
Data assimilation in models representing spatio-temporal phenomena poses a challenge, particularly if the spatial histogram of the variable appears with multiple modes. The traditi…
Bayesian Spatial Inversion and Conjugate Selection Gaussian Prior Models
Henning Omre, Kjartan Rimstad
We introduce the concept of conjugate prior models for a given likelihood function in Bayesian spatial inversion. The conjugate class of prior models can be selection extended and…
Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction
Torstein Fjeldstad, Henning Omre
Efficient assessment of convolved hidden Markov models is discussed. The bottom-layer is defined as an unobservable categorical first-order Markov chain, while the middle-layer is…
Generalized Gaussian Random Fields using hidden selections
Kjartan Rimstad, Henning Omre
We study non-Gaussian random fields constructed by the selection normal distribution, and we term them selection Gaussian random fields. The selection Gaussian random field can cap…
Skew-Gaussian Random Fields
Kjartan Rimstad, Henning Omre
Skewness is often present in a wide range of spatial prediction problems, and modeling it in the spatial context remains a challenging problem. In this study a skew-Gaussian random…