20 citations · 26 across the 7 of their papers we have counts for
8 papers
Spatial forecast postprocessing: The Max-and-Smooth approach
Stefan Siegert, Ben Hooper, Joshua Lovegrove +2
Numerical weather forecasts can exhibit systematic errors due to simplifying model assumptions and computational approximations. Statistical postprocessing is a statistical approac…
Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling
Birgir Hrafnkelsson, Helgi Sigurdarson, Sölvi Rögnvaldsson +3
The power-law rating curve has been used extensively in hydraulic practice and hydrology. It is given by , where is discharge, is water elevation, , a…
Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models
Birgir Hrafnkelsson, Stefan Siegert, Raphaël Huser +2
With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayes…
Approximate Bayesian inference for analysis of spatio-temporal flood frequency data
Árni V. Johannesson, Stefan Siegert, Raphaël Huser +2
Extreme floods cause casualties, and widespread damage to property and vital civil infrastructure. We here propose a Bayesian approach for predicting extreme floods using the gener…
A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology
Giri Gopalan, Birgir Hrafnkelsson, Christopher K. Wikle +4
In 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 simu…
A Bayesian hierarchical model for monthly maxima of instantaneous flow
Egil Ferkingstad, Oli Pall Geirsson, Birgir Hrafnkelsson +2
We 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 MCMC split sampler: A block Gibbs sampling scheme for latent Gaussian models
Óli Páll Geirsson, Birgir Hrafnkelsson, Daniel Simpson +1
A 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 sampl…
Computationally efficient spatial modeling of annual maximum 24 hour precipitation. An application to data from Iceland
Óli Páll Geirsson, Birgir Hrafnkelsson, Daniel Simpson
We 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…