StackedGP – Stacked Gaussian Process

A network of independently trained Gaussian processes (StackedGP) is introduced to integrate different datasets through model composition, enhance predictions of quantities of interest through a cascade of intermediate predictions, and to propagate uncertainties through emulated dynamical systems driven by uncertain forcing variables. By using analytical first and second-order moments of a Gaussian process with uncertain inputs using squared exponential and polynomial kernels, approximated expectations of quantities of interests that require an arbitrary composition of functions can be obtained.

  • K. Abdelfatah, J. Bao, and G. Terejanu, “Geospatial Uncertainty Modeling using Stacked Gaussian Processes,” under review Environmental Modelling & Software, 2017.
    [BibTeX] [Download PDF]
    @article{Abdelfatah_EMS,
    author = {Kareem Abdelfatah and Junshu Bao and Gabriel Terejanu},
    title = {{Geospatial Uncertainty Modeling using Stacked Gaussian Processes}},
    journal = {{under review Environmental Modelling \& Software}},
    url = {https://arxiv.org/abs/1612.02897},
    year = {2017},
    }

  • H. Li, A. Chowdhury, G. Terejanu, A. Chanda, and S. Banerjee, “A Stacked Gaussian Process for Predicting Geographical Incidence of Aflatoxin with Quantified Uncertainties ,” in International Conference on Advances in Geographic Information Systems ACM SIGSPATIAL, Seattle, Washington, 2015.
    [BibTeX]
    @CONFERENCE{LiP_SIGS_2015,
    author = {Hui Li and Asif Chowdhury and Gabriel Terejanu and Anindya Chanda and Sourav Banerjee},
    title = {{A Stacked Gaussian Process for Predicting Geographical Incidence of Aflatoxin with Quantified Uncertainties
    }},
    booktitle = {{International Conference on Advances
    in Geographic Information Systems ACM SIGSPATIAL, Seattle, Washington}},
    year = {2015},
    month= {November},
    }