Non-parametric Bayesian modeling and fusion of spatio-temporal information sources

Published

Journal Article

We propose a Gaussian process (GP) factor analysis approach for modeling multiple spatio-temporal datasets with non-stationary spatial covariance structure. A novel kernel stick-breaking process based mixture of GPs is proposed to address the problem of non-stationary covariance structure. We also propose a joint GP factor analysis approach for simultaneous modeling of multiple heterogenous spatio-temporal datasets. The performance of the proposed models are demonstrated on the analysis of multi-year unemployment rates of various metropolitan cities in the United States and counties in Michigan. © 2011 IEEE.

Duke Authors

Cited Authors

  • Ray, P; Carin, L

Published Date

  • September 13, 2011

Published In

  • Fusion 2011 14th International Conference on Information Fusion

Citation Source

  • Scopus