David Bolin

Universitetslektor at Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

My primary research areas are spatial statistics, spatio-temporal modeling, and computationally efficient inference methods for analysis of large data sets.

Source: chalmers.se

Projects

2014–2019

Material structures seen through microscopes and statistics

Aila Särkkä Applied Mathematics and Statistics
Charlotte Hamngren Blomqvist Eva Olsson Group
Mats Rudemo Applied Mathematics and Statistics
Cecilia Fager Eva Olsson Group
Sandra Eriksson Barman Applied Mathematics and Statistics
Eva Olsson Eva Olsson Group
Holger Rootzen Applied Mathematics and Statistics
David Bolin Applied Mathematics and Statistics
Marco Longfils Applied Mathematics and Statistics
Niklas Lorén Eva Olsson Group
Swedish Foundation for Strategic Research (SSF)

2015–2018

Approximation and simulation of Lévy-driven SPDE

Annika Lang Applied Mathematics and Statistics
Stig Larsson Applied Mathematics and Statistics
Adam Andersson Applied Mathematics and Statistics
David Bolin Applied Mathematics and Statistics
Andreas Petersson Applied Mathematics and Statistics
Swedish Research Council (VR)

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Publications

2016

Spatially adaptive covariance tapering

David Bolin, Jonas Wallin
Spatial Statistics. Vol. 18, p. 163-178
Journal article
2016

Fast Bayesian whole-brain fMRI analysis with spatial 3D priors

David Bolin, Anders Eklund, Per Sidén et al
NeuroImage. Vol. 146 (1), p. 211-225
Journal article
2016

Calibrating regionally downscaled precipitation over Norway through quantile-based approaches

David Bolin, Arnoldo Frigessi, Ida Scheel et al
Advances in Statistical Climatology, Meteorology and Oceanography. Vol. 2, p. 39-47
Journal article
2016

Modeling the Polarimetric mm-wave Propagation Channel using Censored Measurements

C. Gustafson, David Bolin, F. Tufvesson
2016 Ieee Global Communications Conference, p. Article number 7842003-
Paper in proceedings
2016

Quantifying the uncertainty of contour maps

David Bolin, Finn Lindgren
Journal of Computational and Graphical Statistics. Vol. 26 (3), p. 513-524
Journal article
2015

Statistical Prediction of Global Sea Level From Global Temperature

Aila Särkkä, P. Guttorp, M. Novak et al
Statistica Sinica. Vol. 25 (1), p. 351-367
Journal article
2015

Statistical Modeling and Estimation of Censored Pathloss Data

T. Abbas, C. Gustafson, David Bolin et al
IEEE Wireless Communications Letters. Vol. 4 (5), p. 569-572
Journal article
2015

Geostatistical Modelling Using Non-Gaussian Matern Fields

David Bolin, Jonas Wallin
Scandinavian Journal of Statistics. Vol. 42 (3), p. 872-890
Journal article
2015

Excursion and contour uncertainty regions for latent Gaussian models

Finn Lindgren, David Bolin
Journal of the Royal Statistical Society. Series B: Statistical Methodology. Vol. 77 (1), p. 85-106
Journal article
2014

Multivariate latent Gaussian random field mixture models

Finn Lindgren, Jonas Wallin, David Bolin
Preprint
2014

Assessing the Uncertainty in Projecting Local Mean Sea Level from Global Temperature

L. Richardson, A. Zimmerman, H. Podschwit et al
Journal of Applied Meteorology and Climatology. Vol. 53 (9), p. 2163-2170
Journal article
2014

Modeling the cluster decay in mm-wave channels

C. Gustafson, David Bolin, F. Tufvesson
8th European Conference on Antennas and Propagation, EuCAP 2014, p. 804-808
Paper in proceedings
2013

Spatial Matérn Fields Driven by Non-Gaussian Noise

David Bolin
Scandinavian Journal of Statistics
Journal article
2013

Spatial statistik och beräkningsintensiva metoder

David Bolin
Qvintensen (3), p. 9-11
Journal article
2013

A comparison between Markov approximations and other methods for large spatial data sets

F. Lindgren, David Bolin
Computational Statistics and Data Analysis. Vol. 61, p. 7-21
Journal article
2011

Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping

David Bolin, Finn Lindgren
Annals of Applied Statistics. Vol. 5 (1), p. 523-550
Journal article
2010

Non-traditional stochastic models for ocean waves

G. Lindgren, F. Lindgren, David Bolin
European Physical Journal: Special Topics. Vol. 185 (1), p. 209-224
Journal article
2009

Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields

David Bolin, F. Lindgren, L. Eklundh et al
Computational Statistics and Data Analysis. Vol. 53 (8), p. 2885-2896
Journal article