Predictive Models for Point Processes

Abstract

Point process data are commonly observed in fields like healthcare and social science. Designing predictive models for such event streams is an under-explored problem, due to often scarce training data. In this thesis, a multitask point process model via a hierarchical Gaussian Process (GP) is proposed, to leverage statistical strength across multiple point processes. Nonparametric learning functions implemented by a GP, which map from past events to future rates, allow analysis of flexible arrival patterns. To facilitate efficient inference, a sparse construction for this hierarchical model is proposed, and a variational Bayes method is derived for learning and inference. Experimental results are shown on both synthetic data and as well as real electronic health-records data.