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Computationally efficient spatial modeling of annual maximum 24 hour precipitation. An application to data from IcelandMay 27 2014We propose a computationally efficient statistical method to obtain distributional properties of annual maximum 24 hour precipitation on a 1 km by 1 km regular grid over Iceland. A latent Gaussian model is built which takes into account observations, ... More

A Bayesian hierarchical model for monthly maxima of instantaneous flowJun 24 2016We propose a comprehensive Bayesian hierarchical model for monthly maxima of instantaneous flow in river catchments. The Gumbel distribution is used as the probabilistic model for the observations, which are assumed to come from several catchments. Our ... More

The MCMC split sampler: A block Gibbs sampling scheme for latent Gaussian modelsJun 20 2015A novel computationally efficient Markov chain Monte Carlo (MCMC) scheme for latent Gaussian models (LGMs) is proposed in this paper. The sampling scheme is a two block Gibbs sampling scheme designed to exploit the model structure of LGMs. We refer to ... More

Approximate Bayesian inference for spatial flood frequency analysisJul 10 2019Extreme floods cause casualties, widespread property damage, and damage to vital civil infrastructure. Predictions of extreme floods within gauged and ungauged catchments is crucial to mitigate these disasters. A Bayesian framework is proposed for predicting ... More

A Hierarchical Spatio-Temporal Statistical Model for Physical SystemsNov 20 2018In this paper, we extend and analyze a Bayesian hierarchical spatio-temporal model for physical systems. A novelty is to model the discrepancy between the output of a computer simulator for a physical process and the actual process values with a multivariate ... More

How to use the Fast Fourier Transform in Large Finite FieldsJan 15 2011The article contents suggestions on how to perform the Fast Fourier Transform over Large Finite Fields. The technique is to use the fact that the multiplicative groups of specific prime fields are surprisingly composite.