H. Tjelmeland

发表

Mode Jumping Proposals in MCMC

H. Tjelmeland, B. Hegstad, 2001 .

Rejoinder

A. Nobel, G. Parmigiani, R. Scharpf, 2009 .

Exact and Approximate Recursive Calculations for Binary Markov Random Fields Defined on Graphs

Håkon Tjelmeland, Haakon Michael Austad, H. Tjelmeland, 2012 .

Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior

Hugo Lewi Hammer, Arild Buland, Håkon Tjelmeland, 2012 .

C O ] 2 2 M ay 2 01 5 Approximations and bounds for binary Markov random fields

H. Tjelmeland, H. Austad, 2018 .

Construction of binary multi-grid Markov random field models from training images

H. Tjelmeland, Håkon Toftaker, 2012 .

Construction of Binary Multi-grid Markov Random Field Prior Models from Training Images

Håkon Tjelmeland, Håkon Toftaker, H. Tjelmeland, 2013, Mathematical Geosciences.

Markov Random Fields with Higher‐order Interactions

Håkon Tjelmeland, Julian Besag, J. Besag, 1998 .

Using all Metropolis-Hastings proposals to estimate mean values

H. Tjelmeland, Hakon Tjelmeland, 2004 .

Bayesian calibration of hydrocarbon reservoir models using an approximate reservoir simulator in the prior specification

Håkon Tjelmeland, Ole Petter Lødøen, H. Tjelmeland, 2010 .

On the use of local optimizations within Metropolis–Hastings updates

H. Tjelmeland, J. Eidsvik, 2004 .

Ensemble updating of binary state vectors by maximizing the expected number of unchanged components

Margrethe Kvale Loe, H. Tjelmeland, 2020, Scandinavian Journal of Statistics.

Non-linear Bayesian joint inversion of seismic reflection coefficients

Håkon Tjelmeland, Bjørn Ursin, B. Ursin, 2008 .

An approximate forward-backward algorithm applied to binary Markov random fields

H. Tjelmeland, H. Austad, 2011 .

Ensemble updating of binary state vectors by maximizing the expected number of unchanged components

Margrethe Kvale Loe, H. Tjelmeland, 2019, Scandinavian Journal of Statistics.

Speckle Reduction and Maximum Likelihood Classification of Sar Images from Sea Ice Recorded During Mizex 87

H. Tjelmeland, T. Olaussen, H. Tjelmeland, 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

Approximate computations for binary Markov random fields and their use in Bayesian models

H. Tjelmeland, H. Austad, 2016, Statistics and Computing.

Prior specification of neighbourhood and interaction structure in binary Markov random fields

H. Tjelmeland, Petter Arnesen, 2016, Statistics and Computing.

Prior specification for binary Markov mesh models

H. Tjelmeland, Xin Luo, 2018, Statistics and Computing.

A multiple-try Metropolis–Hastings algorithm with tailored proposals

H. Tjelmeland, Xin Luo, 2019, Computational Statistics.

Bayesian modelling of spatial compositional data

Håkon Tjelmeland, H. Tjelmeland, Kjetill Vassmo Lund, 2003 .

Bayesian CART: Prior Specification and Posterior Simulation

Michael A. West, Håkon Tjelmeland, Yuhong Wu, 2007 .

Optimal cache search depends on precision of spatial memory and pilfering, but what if that knowledge is not perfect?

G. Pfuhl, H. Tjelmeland, S. Molden, 2009, Animal Behaviour.

Merging two gene-expression studies via cross-platform normalization

Andrew B. Nobel, Cheng Fan, Charles M. Perou, 2008, Bioinform..

Approximate Bayesian Inference Based on Expected Evaluation

H. Tjelmeland, M. Riegler, H. Hammer, 2023, Bayesian Analysis.

A Bayesian Model for Cross-Study Differential Gene Expression

Giovanni Parmigiani, Håkon Tjelmeland, Andrew B Nobel, 2009, Journal of the American Statistical Association.

Precision and Reliability in Animal Navigation

G. Pfuhl, H. Tjelmeland, R. Biegler, 2011, Bulletin of mathematical biology.

Non-linear Bayesian joint inversion of seismic reflection coefficients

Håkon Tjelmeland, Bjørn Ursin, B. Ursin, 2007 .

Ensemble updating of categorical state vectors

Margrethe Kvale Loe, H. Tjelmeland, 2022, Computational Statistics.

A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image

Xin Luo, Torstein Fjeldstad, Haakon Tjelmeland, 2018, Geophysical Prospecting.

Prior specification for binary Markov mesh models

Xin Luo, Håkon Tjelmeland, H. Tjelmeland, 2017, Stat. Comput..

Prior specification of neighbourhood and interaction structure in binary Markov random fields

Petter Arnesen, Håkon Tjelmeland, H. Tjelmeland, 2015, Stat. Comput..

Semi-Markov Random Fields

Håkon Tjelmeland, Lars Holden, L. Holden, 1993 .

Directional Metropolis-Hastings algorithms on hyperplanes.

Hugo Lewi Hammer, Håkon Tjelmeland, H. Tjelmeland, 2008 .

On directional Metropolis–Hastings algorithms

Jo Eidsvik, Håkon Tjelmeland, H. Tjelmeland, 2006, Stat. Comput..

Stochastic Simulation and Conditioning by Annealing in Reservoir Description

Henning Omre, Håkon Tjelmeland, Bjørn Kåre Hegstad, 1994 .

Simulation of Random Functions on Large Lattices

Henning Omre, Knut Sølna, Håkon Tjelmeland, 1993 .

Overestimation of volatility in schizophrenia and autism? A comparative study using a probabilistic reasoning task

H. Tjelmeland, M. Mittner, R. Biegler, 2019, PloS one.

Geophysics-Based Fluid-Facies Predictions Using Ensemble Updating of Binary State Vectors

Margrethe Kvale Loe, D. Grana, H. Tjelmeland, 2021, Mathematical Geosciences.

Psychosis and Psychotic-Like Symptoms Affect Cognitive Abilities but Not Motivation in a Foraging Task

H. Tjelmeland, G. Pfuhl, Isabel Kreis, 2020, Frontiers in Psychology.

Approximate forward-backward algorithm for a switching linear Gaussian model

Hugo Hammer, Håkon Tjelmeland, H. Tjelmeland, 2011, Comput. Stat. Data Anal..

A sparse matrix formulation of model-based ensemble Kalman filter

H. Tjelmeland, Håkon Gryvill, 2022, Statistics and Computing.

Norges Teknisk-naturvitenskapelige Universitet Control Variates for the Metropolis-hastings Algorithm Control Variates for the Metropolis-hastings Algorithm

Hugo Lewi Hammer, Håkon Tjelmeland, H. Tjelmeland, 2022 .

Merging two gene-expression studies via cross-platform normalization

A. Nobel, C. Fan, C. Perou, 2008, Bioinform..

Norges Teknisk-naturvitenskapelige Universitet Fitting Gaussian Markov Random Fields to Gaussian Fields Fitting Gaussian Markov Random Fields to Gaussian Fields Tmr Project on Spatial Statistics (erb-fmrx-ct960095) for Support and Inspiration

H. Tjelmeland, H. Rue, 2022 .

A multiple-try Metropolis–Hastings algorithm with tailored proposals

Xin Luo, Haakon Tjelmeland, H. Tjelmeland, 2018, Computational Statistics.