2 citations · 2 across the 2 of their papers we have counts for
6 papers
Scalable likelihood-based inference for limited dependent variable models
David T. Frazier, Ruben Loaiza-Maya, Didier Nibbering
Limited dependent variable models are central to empirical economics, but likelihood-based inference is infeasible when likelihoods involve high-dimensional integration over latent…
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…
Preconditioned Robust Neural Posterior Estimation for Misspecified Simulators
Ryan P. Kelly, David T. Frazier, David J. Warne +1
Simulation-based inference (SBI) enables parameter estimation for complex stochastic models with intractable likelihoods when model simulation is feasible. Neural posterior estimat…
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…
Sequential Scoring Rule Evaluation for Forecast Method Selection
David T. Frazier, Donald S. Poskitt
This paper shows that sequential statistical analysis techniques can be generalised to the problem of selecting between alternative forecasting methods using scoring rules. A retur…
Posterior risk of modular and semi-modular Bayesian inference
David T. Frazier, David J. Nott
Modular Bayesian methods perform inference in models that are specified through a collection of coupled sub-models, known as modules. These modules often arise from modelling diffe…