Abstract:
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In cases where field (or experimental) measurements are not available, computer codes can model real physical or engineering systems to reproduce their outcomes. They are usually calibrated in light of experimental data to better reproduce the real system. Statistical methods, based on Gaussian processes, for calibration and prediction have been especially important when the computer codes are expensive and experimental data limited. In this paper, we develop the Bayesian treed calibration (BTC) as an extension of standard Gaussian process calibration methods to deal with non-stationarity computer codes and/or their discrepancy from the field (or experimental) data. Our proposed method partitions both the calibration and observable input space, based on a binary tree partitioning, into subregions where existing model calibration methods can be applied to link a computer code with the real system. The estimation of the parameters in the proposed model is carried out using Markov chain Monte Carlo (MCMC) computational techniques. Different strategies have been applied to improve mixing.
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