156 citations · 361 across the 32 of their papers we have counts for
10 papers · 1 filter
Bayesian Prediction with Covariates Subject to Detection Limits
Caroline Svahn, Mattias Villani
Missing values in covariates due to censoring by signal interference or lack of sensitivity in the measuring devices are common in industrial problems. We propose a full Bayesian s…
Local Prediction Pools
Oscar Oelrich, Mattias Villani, Sebastian Ankargren
We propose local prediction pools as a method for combining the predictive distributions of a set of experts conditional on a set of variables believed to be related to the predict…
Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes
Mattias Villani, Matias Quiroz, Robert Kohn +1
Spectral subsampling MCMC was recently proposed to speed up Markov chain Monte Carlo (MCMC) for long stationary univariate time series by subsampling periodogram observations in th…
Anatomically informed Bayesian spatial priors for fMRI analysis
David Abramian, Per Sidén, Hans Knutsson +2
Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical bounda…
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone, Matias Quiroz, Robert Kohn +2
Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datasets has developed rapidly in recent years. However, the underlying methods are generally limited to relativel…
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Per Sidén, Finn Lindgren, David Bolin +2
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors has been shown to produce state-of-the-art activity maps…
Subsampling MCMC - An introduction for the survey statistician
Matias Quiroz, Mattias Villani, Robert Kohn +2
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical i…
Tree Ensembles with Rule Structured Horseshoe Regularization
Malte Nalenz, Mattias Villani
We propose a new Bayesian model for flexible nonlinear regression and classification using tree ensembles. The model is based on the RuleFit approach in Friedman and Popescu (2008)…
Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator
Matias Quiroz, Mattias Villani, Robert Kohn
We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly…
Speeding Up MCMC by Efficient Data Subsampling
Matias Quiroz, Robert Kohn, Mattias Villani +1
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for observations is estimated from a random subset of observations. W…