1.7k citations · 4.6k across the 36 of their papers we have counts for
10 papers · 1 filter
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
Charles C. Margossian, Aki Vehtari, Daniel Simpson +1
Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as…
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…
Rank-normalization, folding, and localization: An improved for assessing convergence of MCMC
Aki Vehtari, Andrew Gelman, Daniel Simpson +2
Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this pape…
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…
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…
Scalable iterative methods for sampling from massive Gaussian random vectors
Daniel P. Simpson, Ian W. Turner, Christopher M. Strickland +1
Sampling from Gaussian Markov random fields (GMRFs), that is multivariate Gaussian ran- dom vectors that are parameterised by the inverse of their covariance matrix, is a fundament…