110 citations · 142 across the 9 of their papers we have counts for
6 papers · 1 filter
Modularized Bayesian analyses and cutting feedback in likelihood-free inference
Atlanta Chakraborty, David J. Nott, Christopher Drovandi +2
There has been much recent interest in modifying Bayesian inference for misspecified models so that it is useful for specific purposes. One popular modified Bayesian inference meth…
Population Calibration using Likelihood-Free Bayesian Inference
Christopher Drovandi, Brodie Lawson, Adrianne L Jenner +1
In this paper we develop a likelihood-free approach for population calibration, which involves finding distributions of model parameters when fed through the model produces a set o…
Robust Approximate Bayesian Computation: An Adjustment Approach
David T. Frazier, Christopher Drovandi, Ruben Loaiza-Maya
We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally appli…
Robust Approximate Bayesian Inference with Synthetic Likelihood
David T. Frazier, Christopher Drovandi
Bayesian synthetic likelihood (BSL) is now an established method for conducting approximate Bayesian inference in models where, due to the intractability of the likelihood function…
A synthetic likelihood-based Laplace approximation for efficient design of biological processes
Mahasen Dehideniya, Antony M. Overstall, Chris C. Drovandi +1
Complex models used to describe biological processes in epidemiology and ecology often have computationally intractable or expensive likelihoods. This poses significant challenges…
Bayesian Indirect Inference Using a Parametric Auxiliary Model
Christopher C. Drovandi, Anthony N. Pettitt, Anthony Lee
Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric (auxiliary) model that is both…