Spatio-temporal change-point detection and constrained Bayesian optimization

Abstract

This thesis makes contributions to two research topics: spatio-temporal change-point detection and constrained Bayesian optimization. Spatio-temporal change-point detection is concerned with detecting statistical anomalies based on multiple data streams collected at different locations. In Chapter 2 and Chapter 3, we address two challenges in spatio-temporal change-point detection: (i) how to deal with data with high dimensionality, and (ii) how to capture spatial and temporal correlations. Bayesian optimization is a prevalent approach for optimization problems defined by expensive-to-evaluate black-box functions. In Chapter 4, we develop a practical algorithm for optimization problems with black-box objective function and constraints.