MOGPTK: The Multi-Output Gaussian Process Toolkit

• URL: http://arxiv.org/abs/2002.03471v1
• Date: Sun, 9 Feb 2020 23:34:49 GMT
• Title: MOGPTK: The Multi-Output Gaussian Process Toolkit
• Authors: Taco de Wolff and Alejandro Cuevas and Felipe Tobar
• Abstract summary: We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP) The aim of this toolkit is to make multi-output GP (MOGP) models accessible to researchers, data scientists, and practitioners alike.
• Score: 71.08576457371433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP). The aim of this toolkit is to make multi-output GP (MOGP) models accessible to researchers, data scientists, and practitioners alike. MOGPTK uses a Python front-end, relies on the GPflow suite and is built on a TensorFlow back-end, thus enabling GPU-accelerated training. The toolkit facilitates implementing the entire pipeline of GP modelling, including data loading, parameter initialization, model learning, parameter interpretation, up to data imputation and extrapolation. MOGPTK implements the main multi-output covariance kernels from literature, as well as spectral-based parameter initialization strategies. The source code, tutorials and examples in the form of Jupyter notebooks, together with the API documentation, can be found at http://github.com/GAMES-UChile/mogptk

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