# Simulation-Based Fitting of Intractable Models via Sequential Sampling and Local Smoothing

arXiv: https://arxiv.org/abs/2511.08180 · HTML: https://mlsanity.com/paper/2511.08180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [ABC random forests for Bayesian parameter inference](https://mlsanity.com/paper/1605.05537/) (2016)
- [The ABC of Simulation Estimation with Auxiliary Statistics](https://mlsanity.com/paper/1501.01265/) (2015)
- [BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood](https://mlsanity.com/paper/1907.10940/) (2019)

## Abstract (arXiv)

This paper presents a comprehensive algorithm for fitting generative models whose likelihood, moments, and other quantities typically used for inference are not analytically or numerically tractable. The proposed method aims to provide a general solution that requires only limited prior information on the model parameters. The algorithm combines a global search phase, aimed at identifying the region of the solution, with a local search phase that mimics a trust region version of the Fisher scoring algorithm for computing a quasi-likelihood estimator. Comparisons with alternative methods demonstrate the strong performance of the proposed approach. An R package implementing the algorithm is available on CRAN.