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Research activity at Architecture explores a wide variety of significant research areas within architecture and the built environment. Among these is the better exploitation of innovative construction technologies and ICT to optimise 'total building performance', as well as reduce waste and environmental impact. Sustainable architectural and urban design is an important component of this. To this end, the Cluster for Research in Design and Sustainability (CRiDS) focuses its research energies towards developing resilient responses to the social, environmental and economic challenges associated with urbanism and cities, in both the developed and developing world.

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Time-series Gaussian process regression based on toeplitz computation of O(N2) operations and O(N)-level storage

Zhang, Y. and Leithead, W.E. and Leith, D.J. (2005) Time-series Gaussian process regression based on toeplitz computation of O(N2) operations and O(N)-level storage. In: Proceedings of the 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. IEEE, pp. 3711-3716. ISBN 0-7803-9567-0

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Gaussian process (GP) regression is a Bayesian nonparametric model showing good performance in various applications. However, its hyperparameter-estimating procedure may contain numerous matrix manipulations of O(N3) arithmetic operations, in addition to the O(N2)-level storage. Motivated by handling the real-world large dataset of 24000 wind-turbine data, we propose in this paper an efficient and economical Toeplitz-computation scheme for time-series Gaussian process regression. The scheme is of O(N2) operations and O(N)-level memory requirement. Numerical experiments substantiate the effectiveness and possibility of using this Toeplitz computation for very large datasets regression (such as, containing 10000~100000 data points).