35 citations · 50 across the 3 of their papers we have counts for
3 papers
stat.CO2012★ 5 cited
Laplace approximation for logistic Gaussian process density estimation and regression
Jaakko Riihimäki, Aki Vehtari
Logistic Gaussian process (LGP) priors provide a flexible alternative for modelling unknown densities. The smoothness properties of the density estimates can be controlled through…
stat.ML2012★ 10 cited
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…
stat.ML2012★ 35 cited
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…