18 citations · 67 across the 26 of their papers we have counts for
6 papers · 1 filter
ABC-based Forecasting in State Space Models
Chaya Weerasinghe, Ruben Loaiza-Maya, Gael M. Martin +1
Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in som…
Solving the Forecast Combination Puzzle
David T. Frazier, Ryan Covey, Gael M. Martin +1
We demonstrate that the forecasting combination puzzle is a consequence of the methodology commonly used to produce forecast combinations. By the combination puzzle, we refer to th…
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…
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Ryan P. Kelly, David J. Nott, David T. Frazier +2
Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods. While traditional statistical app…
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…