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Activity Number: 69
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #315310 View Presentation
Title: Multivariate Stochastic Process Models for Joint Regression and Classification
Author(s): Tony Pourmohamad* and Herbert Lee
Companies: UC Santa Cruz and UC Santa Cruz
Keywords: Bayesian Statistics ; Multivariate Gaussian Process ; Regression ; Classification ; Particle Learning ; Computer Experiments
Abstract:

In many scenarios, the need for models that consider both continuous and binary outputs and their correlations is required but lacking. In response to this problem, we develop a new modeling framework for joint regression and classification based upon multivariate stochastic processes. In particular, we extend established methodology for modeling of continuous multivariate spatial outputs, via a separable multivariate Gaussian process, by adding a latent process structure that allows for joint modeling of continuous and binary outputs. In addition, we implement inference using particle learning, which allows us to conduct fast sequential inference in our model. We demonstrate the effectiveness of our proposed methods on both synthetic examples and a real world hydrology computer experiment problem.


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