Local Gaussian Process Model for Large-Scale Dynamic Computer Experiments
| Autor: | C. Devon Lin, Ru Zhang, Pritam Ranjan |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Computer science Scalar (mathematics) 02 engineering and technology Computer experiment 01 natural sciences k-nearest neighbors algorithm Methodology (stat.ME) 010104 statistics & probability symbols.namesake Sequential analysis Singular value decomposition 0202 electrical engineering electronic engineering information engineering symbols Discrete Mathematics and Combinatorics 020201 artificial intelligence & image processing 0101 mathematics Statistics Probability and Uncertainty Gaussian process Algorithm Statistics - Methodology |
| Zdroj: | Journal of Computational and Graphical Statistics. 27:798-807 |
| ISSN: | 1537-2715 1061-8600 |
| Popis: | The recent accelerated growth in the computing power has generated popularization of experimentation with dynamic computer models in various physical and engineering applications. Despite the extensive statistical research in computer experiments, most of the focus had been on the theoretical and algorithmic innovations for the design and analysis of computer models with scalar responses. In this paper, we propose a computationally efficient statistical emulator for a large-scale dynamic computer simulator (i.e., simulator which gives time series outputs). The main idea is to first find a good local neighbourhood for every input location, and then emulate the simulator output via a singular value decomposition (SVD) based Gaussian process (GP) model. We develop a new design criterion for sequentially finding this local neighbourhood set of training points. Several test functions and a real-life application have been used to demonstrate the performance of the proposed approach over a naive method of choosing local neighbourhood set using the Euclidean distance among design points. 32 pages, 7 figures |
| Databáze: | OpenAIRE |
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