Open Access
September 2016 Multivariate Stochastic Process Models for Correlated Responses of Mixed Type
Tony Pourmohamad, Herbert K. H. Lee
Bayesian Anal. 11(3): 797-820 (September 2016). DOI: 10.1214/15-BA976


We propose a new model for correlated outputs of mixed type, such as continuous and binary outputs, with a particular focus on joint regression and classification, motivated by an application in constrained optimization for computer simulation modeling. Our framework is based upon multivariate stochastic processes, extending Gaussian process methodology for modeling of continuous multivariate spatial outputs by adding a latent process structure that allows for joint modeling of a variety of types of correlated outputs. In addition, we implement fully Bayesian inference using particle learning, which allows us to conduct fast sequential inference. We demonstrate the effectiveness of our proposed methods on both synthetic examples and a real world hydrology computer experiment optimization problem where it is helpful to model the black box objective function as correlated with satisfaction of the constraint.


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Tony Pourmohamad. Herbert K. H. Lee. "Multivariate Stochastic Process Models for Correlated Responses of Mixed Type." Bayesian Anal. 11 (3) 797 - 820, September 2016.


Published: September 2016
First available in Project Euclid: 8 October 2015

zbMATH: 1359.62402
MathSciNet: MR3498046
Digital Object Identifier: 10.1214/15-BA976

Keywords: Bayesian statistics , computer simulation experiment , constrained optimization , Gaussian process , particle learning

Rights: Copyright © 2016 International Society for Bayesian Analysis

Vol.11 • No. 3 • September 2016
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