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Comparisons of Split-linear Fitting of Wind Curves
Volume 4, Issue 4 (2006), pp. 497–509
Philippe C. Besse   Nathalie Raimbault  

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https://doi.org/10.6339/JDS.2006.04(4).302
Pub. online: 13 July 2021      Type: Research Article      Open accessOpen Access

Published
13 July 2021

Abstract

Abstract: The detection of slope change points in wind curves depends on linear curve-fitting. Hall and Titterington’s algorithm based on smoothing is adapted and compared to a Bayesian method of curve-fitting. After prior spline smoothing of the data, the algorithms are tested and the errors between the split-linear fitted wind and the real one are estimated. In our case, the adaptation of the edge-preserving smoothing algorithm gives the same good performance as automatic Bayesian curve-fitting based on a Monte Carlo Markov chain algorithm yet saves computation time.

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Keywords
Bayesian curve-fitting MCMC slope change detection

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