# ABC and Indirect Inference

arXiv: https://arxiv.org/abs/1803.01999 · HTML: https://mlsanity.com/paper/1803.01999/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [Neural Methods for Amortized Inference](https://mlsanity.com/paper/2404.12484/) (2024)
- [Simulation-Based Inference via Regression Projection and Batched Discrepancies](https://mlsanity.com/paper/2602.03613/) (2026)

## Abstract (arXiv)

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.