59 citations · 439 across the 69 of their papers we have counts for
26 papers · 1 filter
Weighted Fisher divergence for high-dimensional Gaussian variational inference
Aoxiang Chen, David J. Nott, Linda S. L. Tan
Bayesian inference has many advantages for complex models, but standard Monte Carlo methods for summarizing the posterior can be computationally demanding, and it is attractive to…
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
Nhat-Minh Nguyen, Minh-Ngoc Tran, Christopher Drovandi +1
The Bayesian Synthetic Likelihood (BSL) method is a widely-used tool for likelihood-free Bayesian inference. This method assumes that some summary statistics are normally distribut…
Structured variational approximations with skew normal decomposable graphical models
Robert Salomone, Xuejun Yu, David J. Nott +1
Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred…
Bayesian score calibration for approximate models
Joshua J Bon, David J Warne, David J Nott +1
Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging si…
Flexible Variational Bayes based on a Copula of a Mixture
David Gunawan, Robert Kohn, David Nott
Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimisation. Variational approximation is usef…
Variational Approximation of Factor Stochastic Volatility Models
David Gunawan, Robert Kohn, David Nott
Estimation and prediction in high dimensional multivariate factor stochastic volatility models is an important and active research area because such models allow a parsimonious rep…