论文标题
多元鹰队过程的完美抽样
Perfect Sampling of Multivariate Hawkes Process
论文作者
论文摘要
作为自我激发霍克斯过程的扩展，多元鹰队过程模型对不同类型的随机事件的过程进行了相互兴奋。在本文中，我们提出了一种可以生成I.I.D.的完美抽样算法。多元鹰队过程的固定样品路径，而没有任何短暂偏置。此外，我们还提供了模型和算法参数中算法复杂性的明确表达，并提供数值方案以找到最佳参数集，以最小化完美采样算法的复杂性。
As an extension of self-exciting Hawkes process, the multivariate Hawkes process models counting processes of different types of random events with mutual excitement. In this paper, we present a perfect sampling algorithm that can generate i.i.d. stationary sample paths of multivariate Hawkes process without any transient bias. In addition, we provide an explicit expression of algorithm complexity in model and algorithm parameters and provide numerical schemes to find the optimal parameter set that minimizes the complexity of the perfect sampling algorithm.
