1 citations · 2 across the 7 of their papers we have counts for
9 papers
An MCMC hypothesis test to check a claimed sampler: applied to a claimed sampler for the G-Wishart distribution
Håkon Tjelmeland, Hanna Bu Kvaløy
Suppose we have a distribution of interest, with density say, and an algorithm claimed to generate samples from . Moreover, assume we have available a Me…
A sparse matrix formulation of model-based ensemble Kalman filter
Håkon Gryvill, Håkon Tjelmeland
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…
Ensemble updating of binary state vectors by maximising the expected number of unchanged components
Margrethe Kvale Loe, Håkon Tjelmeland
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 e…
A multiple-try Metropolis-Hastings algorithm with tailored proposals
Xin Luo, Håkon Tjelmeland
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 giv…
A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image
Håkon Tjelmeland, Xin Luo, Torstein Fjeldstad
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 dist…
Prior specification for binary Markov mesh models
Håkon Tjelmeland, Xin Luo
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 fo…
Prior specification of neighbourhood and interaction structure in binary Markov random fields
Petter Arnesen, Håkon Tjelmeland
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 highe…
Fully Bayesian binary Markov random field models: Prior specification and posterior simulation
Petter Arnesen, Håkon Tjelmeland
We propose a flexible prior model for the parameters of binary Markov random fields (MRF) defined on rectangular lattices and with maximal cliques defined from a template maximal c…
Approximations and bounds for binary Markov random fields
Haakon Michael Austad, Håkon Tjelmeland
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 intractabl…