59 citations · 439 across the 69 of their papers we have counts for
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Approximate Bayesian Computation via Regression Density Estimation
Y. Fan, D. J. Nott, S. A. Sisson
Approximate Bayesian computation (ABC) methods, which are applicable when the likelihood is difficult or impossible to calculate, are an active topic of current research. Most curr…
A stochastic variational framework for fitting and diagnosing generalized linear mixed models
Linda S. L. Tan, David J. Nott
In stochastic variational inference, the variational Bayes objective function is optimized using stochastic gradient approximation, where gradients computed on small random subsets…
Variational Inference for Generalized Linear Mixed Models Using Partially Noncentered Parametrizations
Linda S. L. Tan, David J. Nott
The effects of different parametrizations on the convergence of Bayesian computational algorithms for hierarchical models are well explored. Techniques such as centering, noncenter…
Discussions on Fernhead and Prangle (2012)
D. J. Nott, Y. Fan, S. A. Sisson
Two contributions to the discussion of Fearnhead P. and D. Prangle (2012). Constructing summary statistics for approximate Bayesian computation: Semi-automatic approx- imate Bayesi…