18 citations · 67 across the 26 of their papers we have counts for
41 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…
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…
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…
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…
Bayesian Forecasting in Economics and Finance: A Modern Review
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn +6
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem…
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…
Testing model specification in approximate Bayesian computation
Andrés Ramírez-Hassan, David T. Frazier
We present a procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation. We demonstrate theoretically, and empir…
Computing Bayes: From Then 'Til Now'
Gael M. Martin, David T. Frazier, Christian P. Robert
This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first co…
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…
The Impact of Sampling Variability on Estimated Combinations of Distributional Forecasts
Ryan Zischke, Gael M. Martin, David T. Frazier +1
We investigate the performance and sampling variability of estimated forecast combinations, with particular attention given to the combination of forecast distributions. Unknown pa…
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…
Cutting feedback and modularized analyses in generalized Bayesian inference
David T. Frazier, David J. Nott
This work considers Bayesian inference under misspecification for complex statistical models comprised of simpler submodels, referred to as modules, that are coupled together. Such…
Approximating Bayes in the 21st Century
Gael M. Martin, David T. Frazier, Christian P. Robert
The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain intractable statist…
ABC of the Future
Henri Pesonen, Umberto Simola, Alvaro Köhn-Luque +8
Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are…
Variational Bayes in State Space Models: Inferential and Predictive Accuracy
David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin
Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that,…
Loss-Based Variational Bayes Prediction
David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin +1
We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional…
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…
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…
Weak Identification in Discrete Choice Models
David T. Frazier, Eric Renault, Lina Zhang +1
We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, w…