2 citations · 2 across the 1 of their papers we have counts for
5 papers · 1 filter
Pooling information in likelihood-free inference
David T. Frazier, Christopher Drovandi, Lucas Kock +1
Likelihood-free inference (LFI) methods, such as approximate Bayesian computation, have become commonplace for conducting inference in complex models. Many approaches are based on…
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…
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…
Posterior risk of modular and semi-modular Bayesian inference
David T. Frazier, David J. Nott
Modular Bayesian methods perform inference in models that are specified through a collection of coupled sub-models, known as modules. These modules often arise from modelling diffe…