ABC and Indirect Inference
Computation
2018-03-07 v1 Methodology
Abstract
This chapter will appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). Indirect inference (II) is a classical likelihood-free approach that pre-dates the main developments of ABC and relies on simulation from a parametric model of interest to determine point estimates of the parameters. It is not surprising then that some likelihood-free Bayesian approaches have harnessed the II literature. This chapter provides an introduction to II and details the connections between ABC and II. A particular focus is placed on the use of an auxiliary model with a tractable likelihood function, an approach commonly adopted in the II literature, to facilitate likelihood-free Bayesian inferences.
Cite
@article{arxiv.1803.01999,
title = {ABC and Indirect Inference},
author = {Christopher C Drovandi},
journal= {arXiv preprint arXiv:1803.01999},
year = {2018}
}