# Overview of Approximate Bayesian Computation

arXiv: https://arxiv.org/abs/1802.09720 · HTML: https://mlsanity.com/paper/1802.09720/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models](https://mlsanity.com/paper/2210.04872/) (2022)
- [Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers](https://mlsanity.com/paper/2312.02997/) (2023)
- [MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories](https://mlsanity.com/paper/2106.01808/) (2021)
- [Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference](https://mlsanity.com/paper/2306.15731/) (2023)
- [Simulation-Based Empirical Bayes](https://mlsanity.com/paper/2607.21843/) (2026)
- [Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches](https://mlsanity.com/paper/2605.30516/) (2026)

## Abstract (arXiv)

This Chapter, "Overview of Approximate Bayesian Computation", is to appear as the first chapter in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind ABC methods with many examples and illustrations.