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In this paper we present a novel method for emulating a stochastic, or random output, computer model and show its application to a complex rabies model. The method is evaluated both in terms of accuracy and computational efficiency on synthetic data and the rabies model. We address the issue of experimental design and provide empirical evidence on the effectiveness of utilizing replicate model evaluations compared to a space-filling design. We employ the Mahalanobis error measure to validate the heteroscedastic Gaussian process based emulator predictions for both the mean and (co)variance. The emulator allows efficient screening to identify important model inputs and better understanding of the complex behaviour of the rabies model.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • [9] A. Singer, F. Kauhala, K. Holmala, and G.C. Smith. Rabies risk in raccoon dogs and foxes. Developments in Biologicals, 131:213{222, 2008.
    • 193 [10] M. Yuan and G. Wahba. Doubly penalized likelihood estimator in het-
    • 194 eroscedastic regression. Statistics and Probability Letters, 69:11{20, 2004.
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