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

Publication ,  Journal Article
Ray, P; Carin, L
Published in: Fusion 2011 - 14th International Conference on Information Fusion
September 13, 2011

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 Scholars

Published In

Fusion 2011 - 14th International Conference on Information Fusion

Publication Date

September 13, 2011
 

Citation

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Ray, P., & Carin, L. (2011). Non-parametric Bayesian modeling and fusion of spatio-temporal information sources. Fusion 2011 - 14th International Conference on Information Fusion.
Ray, P., and L. Carin. “Non-parametric Bayesian modeling and fusion of spatio-temporal information sources.” Fusion 2011 - 14th International Conference on Information Fusion, September 13, 2011.
Ray P, Carin L. Non-parametric Bayesian modeling and fusion of spatio-temporal information sources. Fusion 2011 - 14th International Conference on Information Fusion. 2011 Sep 13;
Ray, P., and L. Carin. “Non-parametric Bayesian modeling and fusion of spatio-temporal information sources.” Fusion 2011 - 14th International Conference on Information Fusion, Sept. 2011.
Ray P, Carin L. Non-parametric Bayesian modeling and fusion of spatio-temporal information sources. Fusion 2011 - 14th International Conference on Information Fusion. 2011 Sep 13;

Published In

Fusion 2011 - 14th International Conference on Information Fusion

Publication Date

September 13, 2011