论文标题
离散事件数据的贝叶斯半参数长期内存模型
Bayesian semiparametric long memory models for discretized event data
论文作者
论文摘要
我们引入了一类新的半参数潜在变量模型，以用于长期内存离散的事件数据。提出的方法是由亚马逊雨林中鸟类发声的研究引起的。发声的时机表现出自相似性和远距离依赖性根据泊松过程排除模型。所提出的一类分数概率（FRAP）模型基于潜在过程的阈值，该过程由平滑的高斯工艺具有分数布朗尼运动的添加剂膨胀。我们使用马尔可夫链蒙特卡洛（Monte Carlo）开发了一种贝叶斯的推理方法，并在模拟研究中表现出良好的表现。将方法应用于亚马逊鸟发声数据，我们找到了自相似性和非马克维亚/泊松动力学的大量证据。为了适应鸟类发声数据，其中有许多不同种类的鸟类表现出自己的发声动态，在补充材料中提供了FRAP的层次扩展。
We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence ruling out models based on Poisson processes. The proposed class of FRActional Probit (FRAP) models is based on thresholding of a latent process consisting of an additive expansion of a smooth Gaussian process with a fractional Brownian motion. We develop a Bayesian approach to inference using Markov chain Monte Carlo, and show good performance in simulation studies. Applying the methods to the Amazon bird vocalization data, we find substantial evidence for self-similarity and non-Markovian/Poisson dynamics. To accommodate the bird vocalization data, in which there are many different species of birds exhibiting their own vocalization dynamics, a hierarchical expansion of FRAP is provided in Supplementary Materials.
