Projects per year
Abstract
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” statistical problems that challenge exact methods like Markov chain Monte Carlo: for instance, models with unavailable likelihoods, highdimensional models and models featuring large data sets. These approximate methods are the subject of this review. The aim is to help new researchers in particular—and more generally those interested in adopting a Bayesian approach to empirical work—distinguish between different approximate techniques, understand the sense in which they are approximate, appreciate when and why particular methods are useful and see the ways in which they can can be combined.
Original language  English 

Number of pages  26 
Journal  Statistical Science 
DOIs  
Publication status  Accepted/In press  2023 
Keywords
 Approximate Bayesian inference
 intractable Bayesian problems
 approximate Bayesian computation
 Bayesian synthetic likelihood
 variational Bayes
 integrated nested Laplace approximation

Lossbased Bayesian Prediction
Martin, G., Frazier, D., Hyndman, R. & Maneesoonthorn, O.
19/06/20 → 18/06/24
Project: Research

Consequences of Model Misspecification in Approximate Bayesian Computation
Australian Research Council (ARC)
1/02/20 → 1/02/24
Project: Research

The Validation of Approximate Bayesian Computation: Theory and Practice
Martin, G., Frazier, D., Renault, E. & Robert, C.
Australian Research Council (ARC), Monash University, Brown University, Université Paris Dauphine (Paris Dauphine University)
1/02/17 → 31/12/21
Project: Research