Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Tjelmeland, Hakon"'
Autor:
Gryvill, Håkon, Tjelmeland, Håkon
We introduce a computationally efficient variant of the model-based ensemble Kalman filter (EnKF). We propose two changes to the original formulation. First, we phrase the setup in terms of precision matrices instead of covariance matrices, and intro
Externí odkaz:
http://arxiv.org/abs/2210.06021
We propose a generalised framework for the updating of a prior ensemble to a posterior ensemble, an essential yet challenging part in ensemble-based filtering methods. The proposed framework is based on a generalised and fully Bayesian view on the tr
Externí odkaz:
http://arxiv.org/abs/2103.14565
Akademický článek
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In recent years, several ensemble-based filtering methods have been proposed and studied. The main challenge in such procedures is the updating of a prior ensemble to a posterior ensemble at every step of the filtering recursions. In the famous ensem
Externí odkaz:
http://arxiv.org/abs/1904.05107
Akademický článek
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Autor:
Luo, Xin, Tjelmeland, Håkon
We present a new multiple-try Metropolis-Hastings algorithm designed to be especially beneficial when a tailored proposal distribution is available. The algorithm is based on a given acyclic graph $G$, where one of the nodes in $G$, $k$ say, contains
Externí odkaz:
http://arxiv.org/abs/1807.01914
We consider a Bayesian model for inversion of observed amplitude variation with offset (AVO) data into lithology/fluid classes, and study in particular how the choice of prior distribution for the lithology/fluid classes influences the inversion resu
Externí odkaz:
http://arxiv.org/abs/1807.01902
Autor:
Tjelmeland, Håkon, Luo, Xin
We propose prior distributions for all parts of the specification of a Markov mesh model. In the formulation we define priors for the sequential neighborhood, for the parametric form of the conditional distributions and for the parameter values. By s
Externí odkaz:
http://arxiv.org/abs/1707.08339
Discrete Markov random fields form a natural class of models to represent images and spatial data sets. The use of such models is, however, hampered by a computationally intractable normalising constant. This makes parameter estimation and a fully Ba
Externí odkaz:
http://arxiv.org/abs/1501.07414
Autor:
Arnesen, Petter, Tjelmeland, Håkon
In this paper we propose a prior distribution for the clique set and dependence structure of binary Markov random fields (MRFs). In the formulation we allow both pairwise and higher order interactions. We construct the prior by first defining a prior
Externí odkaz:
http://arxiv.org/abs/1501.06344