用输出数据校准随机模拟模型参数，解决不可靠似然与模型误差问题。
Differentiable Calibration of Inexact Stochastic Simulation Models via Kernel Score Minimization
- 通过核得分最小化实现输入参数的可微学习
- 在不精确队列模型上实现参数不确定性量化
- 适合有输出数据但无似然信息的系统建模场景
随机模拟模型是用于辅助决策的生成模型，其可靠性高度依赖于输入参数的校准。然而在实际中，往往只能获取输出层面的数据来学习输入参数，这因模型似然通常不可解析而变得困难。此外，随机模拟模型常存在与真实系统的偏差。现有方法无法仅用输出数据有效学习并量化输入参数的不确定性。本文提出通过核得分最小化结合随机梯度下降，利用输出数据学习可微输入参数，并基于考虑模型不精确性的新渐近正态性结果，采用频数置信集方法量化参数不确定性。该方法在精确与不精确的 G/G/1 队列模型上进行了评估。
原文摘要 · Abstract (English)
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.
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