Coregionalized Single- and Multiresolution Spatially Varying Growth Curve Modeling with Application to Weed Growth

Autor: Banerjee, Sudipto

Publikováno v: Biometrics, 2006 Sep 01. 62(3), 864-876.

Externí odkaz: https://www.jstor.org/stable/4124597

Mixture of multivariate gaussian processes for classification of irregularly sampled satellite image time-series

Autor: Alexandre Constantin, Mathieu Fauvel, Stéphane Girard

Publikováno v: Statistics and Computing
Statistics and Computing, Springer Verlag (Germany), In press
Statistics and Computing, 2022, 32 (5), pp.79. ⟨10.1007/s11222-022-10145-8⟩

International audience; The classification of irregularly sampled Satellite image time-series (SITS) is investigated in this paper. A multivariate Gaussian process mixture model is proposed to address the irregular sampling and the multivariate natur

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::810457c00b57de7c9264a9dd4711469e
https://hal.archives-ouvertes.fr/hal-03280484

Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes

Autor: Heinz Koeppl, Sara Al-Sayed

Publikováno v: IEEE MLSP 2018
(MLSP 2018) 2018 IEEE International Workshop on Machine Learning for Signal Processing
MLSP

In this work, we appropriate the popular tool of Gaussian processes to solve the problem of reconstructing networks from time-series perturbation data. To this end, we propose a construction for multivariateGaussian processes to describe the continuo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b6cb248dd3e3b1375f5e447d23bd8d3
https://doi.org/10.5281/zenodo.1488636

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