Skip to main content
eScholarship
Open Access Publications from the University of California

UCLA

UCLA Electronic Theses and Dissertations bannerUCLA

Applications and Properties of Point Processes

Abstract

This dissertation discusses the properties of point process models for epidemic diseases and other clustered phenomena. We present (1) a novel computationally efficient estimator for the parameters of conditional intensity functions used to model point process data, (2) a comparison of compartmental models and Hawkes-type models for predicting the spread of COVID-19, (3) a potential outcomes framework for point process data, and (4) a novel methodology for bounding the complexity of sparse Boolean-valued tensors represented as point processes, discussed here in the context of tomographic images of fractured silicon materials.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View