# Kernel Recursive ABC: Point Estimation with Intractable Likelihood

arXiv: https://arxiv.org/abs/1802.08404 · HTML: https://mlsanity.com/paper/1802.08404/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

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

- [Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference](https://mlsanity.com/paper/2301.11674/) (2023)
- [Approximate Bayesian Computation Based on Maxima Weighted Isolation Kernel Mapping](https://mlsanity.com/paper/2201.12745/) (2022)

## Abstract (arXiv)

We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why the approach works, showing (for the population setting) that, under a certain assumption, point estimates obtained with this method converge to the true parameter, as recursion proceeds. We have conducted a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that in most cases our method outperforms existing approaches.