156 citations · 361 across the 32 of their papers we have counts for
9 papers · 1 filter
Dynamic Mixture of Experts Models for Online Prediction
Parfait Munezero, Mattias Villani, Robert Kohn
A mixture of experts models the conditional density of a response variable using a mixture of regression models with covariate-dependent mixture weights. We extend the finite mixtu…
Bayesian Optimization of Hyperparameters from Noisy Marginal Likelihood Estimates
Oskar Gustafsson, Mattias Villani, Pär Stockhammar
Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is a popular iterative method where a Gaussian…
Hamiltonian Monte Carlo with Energy Conserving Subsampling
Khue-Dung Dang, Matias Quiroz, Robert Kohn +2
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the…
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…
Fast Bayesian whole-brain fMRI analysis with spatial 3D priors
Per Sidén, Anders Eklund, David Bolin +1
Spatial whole-brain Bayesian modeling of task-related functional magnetic resonance imaging (fMRI) is a great computational challenge. Most of the currently proposed methods theref…
The block-Poisson estimator for optimally tuned exact subsampling MCMC
Matias Quiroz, Minh-Ngoc Tran, Mattias Villani +2
Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is p…
Speeding Up MCMC by Delayed Acceptance and Data Subsampling
Matias Quiroz, Minh-Ngoc Tran, Mattias Villani +1
The complexity of the Metropolis-Hastings (MH) algorithm arises from the requirement of a likelihood evaluation for the full data set in each iteration. Payne and Mallick (2015) pr…
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Johan Dahlin, Mattias Villani, Thomas B. Schön
We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods. Sequential Monte Carlo with approximate Bayesian…
Efficient Bayesian Multivariate Surface Regression
Feng Li, Mattias Villani
Methods for choosing a fixed set of knot locations in additive spline models are fairly well established in the statistical literature. While most of these methods are in principle…