18 citations · 67 across the 26 of their papers we have counts for
21 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
Robust Approximate Bayesian Computation: An Adjustment Approach
David T. Frazier, Christopher Drovandi, Ruben Loaiza-Maya
We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally appli…
Robust and Efficient Approximate Bayesian Computation: A Minimum Distance Approach
David T. Frazier
In many instances, the application of approximate Bayesian methods is hampered by two practical features: 1) the requirement to project the data down to low-dimensional summary, in…
Focused Bayesian Prediction
Ruben Loaiza-Maya, Gael M. Martin, David T. Frazier
We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defi…
Robust Approximate Bayesian Inference with Synthetic Likelihood
David T. Frazier, Christopher Drovandi
Bayesian synthetic likelihood (BSL) is now an established method for conducting approximate Bayesian inference in models where, due to the intractability of the likelihood function…
Approximate Bayesian Forecasting
David T. Frazier, Worapree Maneesoonthorn, Gael M. Martin +1
Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably…
Asymptotic Properties of Approximate Bayesian Computation
David T. Frazier, Gael M. Martin, Christian P. Robert +1
Approximate Bayesian computation allows for statistical analysis in models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distrib…