59 citations · 439 across the 69 of their papers we have counts for
4 papers · 2 filters
An easy-to-use empirical likelihood ABC method
Sanjay Chaudhuri, Subhro Ghosh, David J. Nott +1
Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in some cases it may not be…
Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach
Ziwen An, David J. Nott, Christopher Drovandi
Bayesian synthetic likelihood (BSL) is now a well established method for performing approximate Bayesian parameter estimation for simulation-based models that do not possess a trac…
Bayesian Deep Net GLM and GLMM
Minh-Ngoc Tran, Nghia Nguyen, David Nott +1
Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models i…
High-dimensional ABC
D. J. Nott, V. M. -H. Ong, Y. Fan +1
This Chapter, "High-dimensional ABC", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind extending A…