{"ID":2843367,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08180","arxiv_id":"2511.08180","title":"Simulation-Based Fitting of Intractable Models via Sequential Sampling and Local Smoothing","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.","short_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 paramet...","url_abs":"https://arxiv.org/abs/2511.08180","url_pdf":"https://arxiv.org/pdf/2511.08180v1","authors":"[\"Guido Masarotto\"]","published":"2025-11-11T12:43:18Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"stat.CO\",\"stat.ML\"]","methods":"[]","has_code":false}