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

Priyadip Ray; Lawrence Carin

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

Priyadip Ray, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	Fusion 2011 - 14th International Conference on Information Fusion
State	Published - Sep 13 2011
Externally published	Yes

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title = "Non-parametric Bayesian modeling and fusion of spatio-temporal information sources",

abstract = "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. {\textcopyright} 2011 IEEE.",

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AU - Carin, Lawrence

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Y1 - 2011/9/13

N2 - 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.

AB - 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.

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M3 - Conference contribution

SN - 9781457702679

BT - Fusion 2011 - 14th International Conference on Information Fusion

ER -

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

Abstract

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