156 citations · 361 across the 32 of their papers we have counts for
6 papers · 1 filter
Bayesian Modeling of Effective and Functional Brain Connectivity using Hierarchical Vector Autoregressions
Bertil Wegmann, Anders Lundquist, Anders Eklund +1
Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression (VAR) hierarchical model…
Robust Real-Time Delay Predictions in a Network of High-Frequency Urban Buses
Hector Rodriguez-Deniz, Mattias Villani
Providing transport users and operators with accurate forecasts on travel times is challenging due to a highly stochastic traffic environment. Public transport users are particular…
Modeling Text Complexity using a Multi-Scale Probit
Johan Falkenjack, Mattias Villani, Arne Jönsson
We present a novel model for text complexity analysis which can be fitted to ordered categorical data measured on multiple scales, e.g. a corpus with binary responses mixed with a…
Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis
Josef Wilzén, Anders Eklund, Mattias Villani
Background: Inference from fMRI data faces the challenge that the hemodynamic system that relates neural activity to the observed BOLD fMRI signal is unknown. New Method: We propos…
Bayesian Non-Central Chi Regression For Neuroimaging
Bertil Wegmann, Anders Eklund, Mattias Villani
We propose a regression model for non-central (NC-) distributed functional magnetic resonance imaging (fMRI) and diffusion weighted imaging (DWI) data, with the heteroscedas…
A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes
Anders Eklund, Martin A. Lindquist, Mattias Villani
We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data.…