18 citations · 67 across the 26 of their papers we have counts for
6 papers · 1 filter
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…