基于面板计数数据的高斯过程的变分推断
Variational Inference for Gaussian Process with Panel Count Data
摘要：
我们提出了第一个框架的高斯过程调制泊松过程的时间数据出现在面板计数的形式。当仅在离散时间点观察实验受试者并且仅可获得后续观察时间之间的事件发生次数时，经常出现面板计数数据。事件的确切发生时间戳未知。基于高斯过程调制强度函数的假设，给出了进行高效变分推断的方法。我们推导出一个易于处理的下界，以减轻问题的棘手的证据下界固有的变分推断框架。我们的算法优于经典的方法合成和三个真正的面板计数集。
英文摘要：
We present the first framework for Gaussian-process-modulated Poisson processes when the temporal data appear in the form of panel counts. Panel count data frequently arise when experimental subjects are observed only at discrete time points and only the numbers of occurrences of the events between subsequent observation times are available. The exact occurrence timestamps of the events are unknown. The method of conducting the efficient variational inference is presented, based on the assumption of a Gaussian-process-modulated intensity function. We derive a tractable lower bound to alleviate the problems of the intractable evidence lower bound inherent in the variational inference framework. Our algorithm outperforms classical methods on both synthetic and three real panel count sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
