1.7k citations · 4.6k across the 36 of their papers we have counts for
4 papers · 1 filter
Asynchronous Gibbs Sampling
Alexander Terenin, Daniel Simpson, David Draper
Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. MCMC methods can be difficult to deploy on parallel and distributed systems due to their…
Beyond the Valley of the Covariance Function
Daniel Simpson, Finn Lindgren, Håvard Rue
Discussion of "Cross-Covariance Functions for Multivariate Geostatistics" by Genton and Kleiber [arXiv:1507.08017].
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…
Constructing Priors that Penalize the Complexity of Gaussian Random Fields
Geir-Arne Fuglstad, Daniel Simpson, Finn Lindgren +1
Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides…