Abstract
A multivariate generalization of the emulator technique described by Hankin (2005) is presented in which random multivariate functions may be assessed. In the standard univariate case (Oakley 1999), a Gaussian process, a finite number of observations is made; here, observations of different types are considered. The technique has the property that marginal analysis (that is, considering only a single observation type) reduces exactly to the univariate theory. The associated software is used to analyze datasets from the field of climate change.
Highlights
Many scientific disciplines require the use of complex computer models
The emulator is an established technique that has been used in many fields including Earth systems science (McNeall 2008), oceanography (Challenor, Hankin, and Marsh 2006), and climate science (Warren et al 2008)
The univariate emulator is generalized to the p- variate case
Summary
Many scientific disciplines require the use of complex computer models Such models, known as “simulators”, are valid objects of inference and are often assumed to be random functions and assessed using the Bayesian statistical paradigm (Currin, Mitchell, Morris, and Ylvisaker 1991); in particular, computer models are often assumed to be Gaussian Processes (Oakley and O’Hagan 2002). One tool used to make inferences about simulators under these circumstances is the emulator (Oakley 1999), and the BACCO suite of R packages (Hankin 2005). I present a generalization of the Gaussian Process which allows the technique to be used for multivariate simulator output. I present a generalization of that work in which the roughness lengths of the components of the multivariate process are allowed to differ
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