2 citations · 2 across the 1 of their papers we have counts for
4 papers
Synthetic likelihood in misspecified models
David T. Frazier, Christopher Drovandi, David J. Nott
Bayesian synthetic likelihood is a widely used approach for conducting Bayesian analysis in complex models where evaluation of the likelihood is infeasible but simulation from the…
Bayesian score calibration for approximate models
Joshua J Bon, David J Warne, David J Nott +1
Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging si…
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…
Unbiased and Consistent Nested Sampling via Sequential Monte Carlo
Robert Salomone, Leah F. South, Christopher Drovandi +2
We introduce a new class of sequential Monte Carlo methods which reformulates the essence of the nested sampling method of Skilling (2006) in terms of sequential Monte Carlo techni…