Predictive Models for Point Processes

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Date

2015

Authors

Lian, Wenzhao

Advisors

Dunson, David B

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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.

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Citation

Lian, Wenzhao (2015). Predictive Models for Point Processes. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/11400.

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