论文标题
使用深层模型估算随机泊松强度
Estimating Stochastic Poisson Intensities Using Deep Latent Models
论文作者
论文摘要
我们提出了估计双随机泊松过程的随机强度的方法。对流量轨迹的统计和理论分析表明，这些过程是到达一系列服务系统的高强度流量的合适模型。驱动交通模型的潜在潜在随机强度过程的统计估计涉及一个相当复杂的非线性过滤问题。我们使用深层神经网络开发了一种新颖的模拟方法，以近似由随机强度过程引起的路径测量方法，以解决此非线性滤波问题。我们的仿真研究表明，该方法在样本内估计以及无限服务器队列的样本外性能预测任务上都是非常准确的。
We present methodology for estimating the stochastic intensity of a doubly stochastic Poisson process. Statistical and theoretical analyses of traffic traces show that these processes are appropriate models of high intensity traffic arriving at an array of service systems. The statistical estimation of the underlying latent stochastic intensity process driving the traffic model involves a rather complicated nonlinear filtering problem. We develop a novel simulation methodology, using deep neural networks to approximate the path measures induced by the stochastic intensity process, for solving this nonlinear filtering problem. Our simulation studies demonstrate that the method is quite accurate on both in-sample estimation and on an out-of-sample performance prediction task for an infinite server queue.
