110 citations · 461 across the 50 of their papers we have counts for
17 papers · 1 filter
Bayesian inference for misspecified generative models
David J. Nott, Christopher Drovandi, David T. Frazier
Bayesian inference is a powerful tool for combining information in complex settings, a task of increasing importance in modern applications. However, Bayesian inference with a flaw…
Reliable Bayesian Inference in Misspecified Models
David T. Frazier, Robert Kohn, Christopher Drovandi +1
We provide a general solution to a fundamental open problem in Bayesian inference, namely poor uncertainty quantification, from a frequency standpoint, of Bayesian methods in missp…
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…
Monte Carlo twisting for particle filters
Joshua J Bon, Christopher Drovandi, Anthony Lee
We consider the problem of designing efficient particle filters for twisted Feynman--Kac models. Particle filters using twisted models can deliver low error approximations of stati…
Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localisation
Christopher Drovandi, David J Nott, David T Frazier
Likelihood-free methods are an essential tool for performing inference for implicit models which can be simulated from, but for which the corresponding likelihood is intractable. H…
Strategic model reduction by analysing model sloppiness: a case study in coral calcification
Sarah A. Vollert, Christopher Drovandi, Gloria M. Monsalve-Bravo +1
It can be difficult to identify ways to reduce the complexity of large models whilst maintaining predictive power, particularly where there are hidden parameter interdependencies.…
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…
Analysis of sloppiness in model simulations: unveiling parameter uncertainty when mathematical models are fitted to data
Gloria M. Monsalve-Bravo, Brodie A. J. Lawson, Christopher Drovandi +7
This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. T…
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…
A Comparison of Likelihood-Free Methods With and Without Summary Statistics
Christopher Drovandi, David T Frazier
Likelihood-free methods are useful for parameter estimation of complex models with intractable likelihood functions for which it is easy to simulate data. Such models are prevalent…
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…
Optimal Bayesian design for model discrimination via classification
Markus Hainy, David J. Price, Olivier Restif +1
Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of…
Variational Bayes with Synthetic Likelihood
Victor M-H. Ong, David J. Nott, Minh-Ngoc Tran +2
Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the param…
An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions
Antony M. Overstall, James M. McGree, Christopher C. Drovandi
The generation of decision-theoretic Bayesian optimal designs is complicated by the significant computational challenge of minimising an analytically intractable expected loss func…
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…