59 citations · 439 across the 69 of their papers we have counts for
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Variational inference for cutting feedback in misspecified models
Xuejun Yu, David J. Nott, Michael Stanley Smith
Bayesian analyses combine information represented by different terms in a joint Bayesian model. When one or more of the terms is misspecified, it can be helpful to restrict the use…
Bayesian clustering using random effects models and predictive projections
Yinan Mao, David J. Nott
Linear mixed models are widely used for analyzing hierarchically structured data involving missingness and unbalanced study designs. We consider a Bayesian clustering method that c…
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 Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models
Lucas Kock, Nadja Klein, David J. Nott
Gaussian mixture models are a popular tool for model-based clustering, and mixtures of factor analyzers are Gaussian mixture models having parsimonious factor covariance structure…
Synthetic likelihood in misspecified models
David T. Frazier, Christopher Drovandi, David J. Nott
Bayesian synthetic likelihood is a widely used approach for conducting Bayesian analysis in complex models where evaluation of the likelihood is infeasible but simulation from the…