Stochastic simulation models are generative models that mimic complex systems to help with decision-making. The reliability of these models heavily depends on well-calibrated input model parameters. However, in many practical scenarios, only output-level data are available to learn the input model parameters, which is challenging due to the often intractable likelihood of the stochastic simulation model. Moreover, stochastic simulation models are frequently inexact, with discrepancies between the model and the target system. No existing methods can effectively learn and quantify the uncertainties of input parameters using only output-level data. In this paper, we propose to learn differentiable input parameters of stochastic simulation models using output-level data via kernel score minimization with stochastic gradient descent. We quantify the uncertainties of the learned input parameters using a frequentist confidence set procedure based on a new asymptotic normality result that accounts for model inexactness. The proposed method is evaluated on exact and inexact G/G/1 queueing models.

翻译：随机仿真模型是模拟复杂系统以辅助决策的生成模型。这些模型的可靠性高度依赖于经过良好校准的输入模型参数。然而，在许多实际场景中，仅能通过输出级数据来学习输入模型参数，由于随机仿真模型的似然函数通常难以处理，这使得参数学习极具挑战性。此外，随机仿真模型往往是不精确的，模型与目标系统之间存在差异。现有方法无法仅利用输出级数据有效学习并量化输入参数的不确定性。本文提出通过核评分最小化结合随机梯度下降，利用输出级数据学习随机仿真模型的可微输入参数。我们基于一种考虑模型不精确性的新渐近正态性结果，采用频率学派的置信集方法量化所学输入参数的不确定性。所提方法在精确与不精确的G/G/1排队模型上进行了评估。