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Learning Temporal Evolution of Spatial Dependence from Dynamic Covariance Matrix to Time-dependent Spatial Kernel

The Department of Statistics is proud to present Shiwei Lan, Arizona State University. In this talk, the speaker will introduce two novel statistical methods to learn TESD in various applications. The first is a semi-parametric method modeling TESD as dynamic covariance matrices. A spherical product representation of covariance matrix is introduced to ensure its positive-definiteness along the process. An efficient MCMC algorithm based on the representation is implemented for Bayesian inference. The second is a fully nonparametric generalization of the first model based on spatiotemporal Gaussian process (STGP).

Thursday, October 17, 2019 at 4:00pm to 5:00pm

Donald Bren Hall, 6011
6210 Donald Bren Hall, Irvine, CA 92697

Event Type

Lectures / Presentations, Speakers


General Public, Students, Alumni, Faculty, Staff




Information & Computer Sciences


Event Sponsor

Department of Statistics

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