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Computer model calibration with large nonstationary spatial outputs: application to the calibration of a climate model

Presented by: 
Serge Guillas
Friday 13th April 2018 - 10:00 to 10:30
INI Seminar Room 1
Bayesian calibration of computer models tunes unknown input parameters by comparing outputs to observations. For model outputs distributed over space, this becomes computationally expensive due to the output size. To overcome this challenge, we employ a basis representations of the model outputs and observations: we match these decompositions to efficiently carry out the calibration. In a second step, we incorporate the nonstationary behavior, in terms of spatial variations of both variance and correlations, into the calibration. We insert two INLA-SPDE parameters into the calibration. A synthetic example and a climate model illustration highlight the benefits of our approach.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons