| Gaussian variational approximation with a factor covariance structure VMH Ong, DJ Nott, MS Smith Journal of Computational and Graphical Statistics 27 (3), 465-478, 2018 | 101 | 2018 |
| Variational Bayes with synthetic likelihood VMH Ong, DJ Nott, MN Tran, SA Sisson, CC Drovandi Statistics and Computing 28, 971-988, 2018 | 70 | 2018 |
| Likelihood-free inference in high dimensions with synthetic likelihood VMH Ong, DJ Nott, MN Tran, SA Sisson, CC Drovandi Computational Statistics & Data Analysis 128, 271-291, 2018 | 40 | 2018 |
| High-dimensional ABC DJ Nott, VMH Ong, Y Fan, SA Sisson Handbook of approximate Bayesian computation, 211-241, 2018 | 27 | 2018 |
| Variational inference for sparse spectrum Gaussian process regression LSL Tan, VMH Ong, DJ Nott, A Jasra Statistics and Computing 26, 1243-1261, 2016 | 21 | 2016 |
| A variational Bayes approach to a semiparametric regression using Gaussian process priors VMH Ong, DK Mensah, DJ Nott, S Jo, B Park, T Choi | 7 | 2017 |
| Edge selection for undirected graphs MHV Ong, S Chaudhuri, BA Turlach Journal of Statistical Computation and Simulation 88 (17), 3291-3322, 2018 | 2 | 2018 |
| Flexible online multivariate regression with variational Bayes and the matrix-variate Dirichlet process AJ Victor Meng Hwee Ong, David J. Nott, Taeryon Choi Foundations of Data Science, 2019 | | 2019 |
| Model Selection for Graphical Markov Models V ONG MENG HWEE | | 2014 |
