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.

翻译：本文提出了一种用于拟合生成模型的综合算法，该类模型的似然函数、矩以及其他通常用于推断的量在解析或数值上均难以处理。所提方法旨在提供一种通用解决方案，仅需对模型参数具备有限的先验信息。该算法将全局搜索阶段与局部搜索阶段相结合：全局搜索旨在确定解的区域，局部搜索则模拟费希尔评分算法的信赖域版本，以计算拟似然估计量。与替代方法的比较表明，所提方法具有优越性能。实现该算法的R软件包已在CRAN平台发布。