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

• URL: http://arxiv.org/abs/2511.08180v1
• Date: Wed, 12 Nov 2025 01:45:03 GMT
• Title: Simulation-Based Fitting of Intractable Models via Sequential Sampling and Local Smoothing
• Authors: Guido Masarotto,
• Abstract summary: 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.<n>An R package implementing the algorithm is available on CRAN.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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