110 citations · 461 across the 50 of their papers we have counts for
7 papers · 2 filters
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…