35 citations · 50 across the 3 of their papers we have counts for
3 papers
Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
Arno Solin, Pasi Jylänki, Jaakko Kauramäki +3
In magnetoencephalography (MEG) the conventional approach to source reconstruction is to solve the underdetermined inverse problem independently over time and space. Here we presen…
Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood
Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari
We consider probabilistic multinomial probit classification using Gaussian process (GP) priors. The challenges with the multiclass GP classification are the integration over the no…
Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen +3
Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models…