将因果元模型扩展至非马尔可夫排队系统，实现快速精准推断。
Extending Causal Metamodeling to a non-Markovian Queue
- 用相位型分布近似非指数分布，使元模型适用于非马尔可夫系统
- 在G/M/1队列上实现推理速度提升数量级，结果准确
- 适合需要快速仿真推断的复杂系统建模与决策分析者
离散事件仿真的元模型可在不运行昂贵仿真的情况下近似系统行为。已有工作提出模块化动态贝叶斯网络（MDBNs）——一种可仅用一个训练好的模型估算多种概率与因果查询（PCQs）的元模型——但该方法仅限于马尔可夫系统。本文首次将MDBNs拓展至非马尔可夫队列，通过相位型分布近似非指数分布。此方法带来新挑战：相位数的选择需权衡元模型精度与可计算性、参数高效学习、以及将连续时间仿真离散化时采样间隔的选取。本文提供初步解决方案，实现了首个针对非马尔可夫系统的因果元模型。在G/M/1队列上的实验表明，相比直接仿真，该方法在推理时间上实现数量级加速，同时保持高度准确性。
原文摘要 · Abstract (English)
Metamodels for discrete-event simulations approximate the behavior of simulation models without running expensive simulations. Prior work introduced modular dynamic Bayesian networks (MDBNs) -- a class of metamodels that can estimate a range of probabilistic and causal queries (PCQs) using a single, trained model -- but the method was limited to Markovian systems. In this paper, we initiate an extension of MDBNs to non-Markovian queues by approximating non-exponential distributions using phase-type distributions. This approach raises novel challenges, including balancing metamodeling accuracy and tractability when choosing the number of phases, efficiently learning metamodel parameters, and choosing the sampling interval that is used to approximate a continuous-time simulation by a discrete-time MDBN. We provide preliminary solutions to these challenges, yielding the first causal metamodeling technique for non-Markovian systems. Experiments on a G/M/1 queue demonstrate that the MDBN can produce accurate answers to PCQs with orders-of-magnitude speedup of inference times relative to direct simulation.
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