Håkon Tjelmeland
Background and activities
Håkon Tjelmeland is a Professor at the Department of Mathematical Sciences. He belongs to The Statistics Group. Tjelmeland has a MSc in Industrial Mathematics and a PhD in Statistics, both from NTNU.
Research areas
- Computational and spatial statistics.
- Bayesian modelling and inference.
- Statistical modelling for petroleum reservoir evaluation.
Work Experience
- 2006- Professor in Statistics, NTNU.
- 1997-2006 Associate Professor in Statistics, NTNU.
- 2003-2004 Sabbatical at the Statistical and Applied Mathematical Sciences Institute (SAMSI) and Institute of Statistics and Decision Sciences, Duke University.
- 1992-1996 PhD position in Statistics at the Dept. of Mathematical Sciences, NTNU. The fall of 1994 Visiting Scholar at the Department of Statistics, University of Washington.
- 1988-1996 Research Scientist in the SAND-group (Statistical Analysis of Natural resource Data) at the Norwegian Computing Center.
External Projects
- Participant in the project; Uncertainty in Reservoir Evaluation (URE).
Professor Tjelmeland has supervised 4 graduated doctoral candidates and is currently supervising 2 PhD candidates.
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
Journal publications
- (2019) A multiple-try Metropolis–Hastings algorithm with tailored proposals. Computational statistics (Zeitschrift).
- (2019) Prior specification for binary Markov mesh models. Statistics and computing. vol. 29 (2).
- (2019) A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image. Geophysical Prospecting. vol. 67 (3).
- (2017) Læringsressurser i grunnutdanningen i matematikk - kvalitet, tilgjengelighet og differensiering. Læring om læring. vol. 1.
- (2016) Prior specification of neighbourhood and interaction structure in binary Markov random fields. Statistics and computing.
- (2016) Approximate computations for binary Markov random fields and their use in Bayesian models. Statistics and computing.
- (2015) Fully Bayesian Binary Markov Random Field Models: Prior Specification and Posterior Simulation. Scandinavian Journal of Statistics. vol. 42 (4).
- (2015) Identifying the computational parameters gone awry in psychosis. Lecture Notes in Computer Science (LNCS). vol. 9250.
- (2013) Construction of Binary Multi-grid Markov Random Field Prior Models from Training Images. Mathematical Geosciences. vol. 45 (4).
- (2012) Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior. Geophysical Prospecting. vol. 60 (3).
- (2012) Near optimal prediction from relevant components. Scandinavian Journal of Statistics. vol. 39 (4).
- (2012) Exact and Approximate Recursive Calculations for Binary Markov Random Fields Defined on Graphs. Journal of Computational And Graphical Statistics. vol. 21 (3).
- (2011) Approximate forward–backward algorithm for a switching linear Gaussian model. Computational Statistics & Data Analysis. vol. 55 (1).
- (2011) Precision and Reliability in Animal Navigation. Bulletin of Mathematical Biology. vol. 73 (5).
- (2010) Bayesian calibration of hydrocarbon reservoir models using an approximate reservoir simulator in the prior specification. Statistical Modelling. vol. 10 (1).
- (2009) Optimal cache search depends on precision of spatial memory and pilfering, but what if that knowledge is not perfect?. Animal Behaviour. vol. 78 (4).
- (2009) A Bayesian model for cross-study differential gene expression. Journal of the American Statistical Association. vol. 104 (488).
- (2009) Rejoinder: A Bayesian model for cross-study differential gene expression. Journal of the American Statistical Association. vol. 104.
- (2008) Control variates for the Metropolis--Hastings algorithm. Scandinavian Journal of Statistics. vol. 35.
- (2008) Control variates for the Metropolis-Hastings algorithm. Scandinavian Journal of Statistics. vol. 35.