92 citations · 232 across the 37 of their papers we have counts for
8 papers · 1 filter
Approximation of bayesian Hawkes process models with Inlabru
Francesco Serafini, Finn Lindgren, Mark Naylor
Hawkes process are very popular mathematical tools for modelling phenomena exhibiting a \textit{self-exciting} or \textit{self-correcting} behaviour. Typical examples are earthquak…
The Integrated Nested Laplace Approximation for fitting Dirichlet regression models
Joaquín Martínez-Minaya, Finn Lindgren, Antonio López-Quílez +2
This paper introduces a Laplace approximation to Bayesian inference in Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness…
Efficient Covariance Approximations for Large Sparse Precision Matrices
Per Sidén, Finn Lindgren, David Bolin +1
The use of sparse precision (inverse covariance) matrices has become popular because they allow for efficient algorithms for joint inference in high-dimensional models. Many applic…
Calculating probabilistic excursion sets and related quantities using excursions
David Bolin, Finn Lindgren
The R software package excursions contains methods for calculating probabilistic excursion sets, contour credible regions, and simultaneous confidence bands for latent Gaussian sto…
Bayesian computing with INLA: new features
Thiago G. Martins, Daniel Simpson, Finn Lindgren +1
The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable…
Going off grid: Computationally efficient inference for log-Gaussian Cox processes
Daniel Simpson, Janine Illian, Finn Lindgren +2
This paper introduces a new method for performing computational inference on log-Gaussian Cox processes. The likelihood is approximated directly by making novel use of a continuous…