59 citations · 439 across the 69 of their papers we have counts for
72 papers
Flexible Transformations for Bayesian Score Calibration
Adam Bretherton, Joshua J. Bon, David J. Warne +2
Modern statistical models are growing increasingly complex in an effort to realistically capture system dynamics. Using standard simulation-based inference, these models may be com…
Simulation-based Bayesian inference under model misspecification
Ryan P. Kelly, David J. Warne, David T. Frazier +3
Simulation-based Bayesian inference (SBI) methods are widely used for parameter estimation in complex models where evaluating the likelihood is challenging but generating simulatio…
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…
Fast Variational Boosting for Latent Variable Models
David Gunawan, David Nott, Robert Kohn
We consider the problem of estimating complex statistical latent variable models using variational Bayes methods. These methods are used when exact posterior inference is either in…
Deep mixture of linear mixed models for complex longitudinal data
Lucas Kock, Nadja Klein, David J. Nott
Mixtures of linear mixed models are widely used for modelling longitudinal data for which observation times differ between subjects. In typical applications, temporal trends are de…
Cutting Feedback in Misspecified Copula Models
Michael Stanley Smith, Weichang Yu, David J. Nott +1
In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose condu…