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Code to accompany the article: Tensor-variate Gaussian process regression for efficient emulation of complex systems: comparing separable covariance in outer product and parallel partial emulators. Semochkina, Jackson and Woods.

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TvGP code

This repository contains code to support the paper “Tensor-variate Gaussian process regression for efficient emulation of complex systems: comparing regressor and covariance structures in outer product and parallel partial emulators” by Semochkina1, Jackson2 and Woods1.

The code is structured as follows:

  • each of the folders “influenza_model” and “environmental_model” contains code to generate example results from the paper for the respective simulators;
  • within each of these folders, there is an “*_emulation.R” script that runs the emulation and produces the results;
  • the “code” folder in the parent directory contains common functions;
  • the “code” folders for each model contain specific functions used in the emulation scripts;
  • the “data” folders contain the data used in the emulation scripts.

Footnotes

  1. University of Southampton, Southampton, UK 2

  2. Durham University, Durham, UK

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Code to accompany the article: Tensor-variate Gaussian process regression for efficient emulation of complex systems: comparing separable covariance in outer product and parallel partial emulators. Semochkina, Jackson and Woods.

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