Variational Inference for Gaussian Process with Panel Count Data
This work addresses a specific challenge in statistical modeling for fields like healthcare or social sciences where exact event timestamps are unavailable, representing an incremental advancement in variational inference for Gaussian processes.
The authors tackled the problem of modeling panel count data, where only aggregated event counts between observation times are known, by developing the first Gaussian-process-modulated Poisson process framework with efficient variational inference. Their method outperformed classical approaches on synthetic and three real datasets, achieving concrete improvements in performance metrics.
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.