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Code to implement efficient spatio-temporal Gaussian Process regression via iterative Kalman Filtering. KF is used to resolve the temporal part of the space-time process while, standard GP regression is used for the spatial part

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MarcoTodescato/Efficient-GP-Regression-via-Kalman-Filtering

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Efficient-GP-Regression-via-Kalman-Filtering

Repository containing simple implementation code relative to the homonimous project based on the two papers:

[1] A.Carron, M.Todescato, R.Carli, L.Schenato, G.Pillonetto, Machine Learning meets Kalman Filtering, proceedings of the 55th Conference on Decision and Control 2016, pp. 4594-4599.

[2] M.Todescato, A.Carron, R.Carli, G.Pillonetto, L.Schenato, Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering, ArXiv:1705.01485, submitted to JMLR.

PS. The code, although based on the code used in the mentioned papers, is slightly different from that. It is a later, improved and yet, simplified version of it. Moreover, the code for the implementation of the adaptive approach presented in [2], is still not present here.
File content is pretty self explaning (for brief more in detail view of each file refer to the corresponding help):
  • main.m: contains the main program
  • plotResults.m: produces some sample figures of the computed results
  • data/: contains datasets (colorado rainfall) and additional precomputed data
  • functions/: contains all the necessary functions
Basic Usage: run the main.m file for performing estimation and prediction. plotResults.m (called inside main.m) plots the results

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Code to implement efficient spatio-temporal Gaussian Process regression via iterative Kalman Filtering. KF is used to resolve the temporal part of the space-time process while, standard GP regression is used for the spatial part

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