A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation
提出一种结合随机生成神经网络与经典蒙特卡洛泊松模拟器的双重随机模拟器，用于更准确地建模和仿真服务、物流、金融系统中的随机到达过程，并提供统计保证和实证表现。
For several areas in the domain of operations research and management science, such as service, logistics and supply chain, and financial systems, the randomness of arrivals is one primary source of uncertainty. Appropriately modeling, statistically characterizing, and efficiently simulating the arrival processes are critical for policy and performance evaluation in the related systems. Classic Monte Carlo simulators have advantages in capturing the interpretable “physics” of a stochastic object, whereas neural network–based simulators have advantages in capturing less-interpretable complicated dependence within a high-dimensional distribution. In “A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation,” Zheng, Zheng, and Zhu propose a doubly stochastic simulator that integrates a stochastic generative neural network and a classic Monte Carlo Poisson simulator to utilize the advantages of both. They provide statistical guarantees and demonstrate empirical performances of the proposed methods.