59 citations · 79 across the 3 of their papers we have counts for
3 papers
Gaussian variational approximation with a factor covariance structure
Victor M. -H. Ong, David J. Nott, Michael S. Smith
Variational approximation methods have proven to be useful for scaling Bayesian computations to large data sets and highly parametrized models. Applying variational methods involve…
Variational Bayes with Synthetic Likelihood
Victor M-H. Ong, David J. Nott, Minh-Ngoc Tran +2
Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the param…
Variational inference for sparse spectrum Gaussian process regression
Linda S. L. Tan, Victor M. H. Ong, David J. Nott +1
We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation. Our a…