64 citations · 88 across the 2 of their papers we have counts for
4 papers
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
Aki Vehtari, Andrew Gelman, Tuomas Sivula +7
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to…
Approximate Inference for Nonstationary Heteroscedastic Gaussian process Regression
Ville Tolvanen, Pasi Jylänki, Aki Vehtari
This paper presents a novel approach for approximate integration over the uncertainty of noise and signal variances in Gaussian process (GP) regression. Our efficient and straightf…
Expectation Propagation for Neural Networks with Sparsity-promoting Priors
Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We pre…
Gaussian Process Regression with a Student-t Likelihood
Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari
This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model. The challenge with the Student-t model is the analyt…