论文标题
Mogptk：多输出高斯流程工具包
MOGPTK: The Multi-Output Gaussian Process Toolkit
论文作者
论文摘要
我们提出了Mogptk，这是一种使用高斯流程（GP）的多通道数据建模的Python软件包。该工具包的目的是使研究人员，数据科学家和从业人员都可以访问多输出GP（MOGP）模型。 Mogptk使用Python前端，依赖于GPFlow套件，并建立在张量后端端，从而实现了GPU加速训练。该工具包有助于实施全GP建模的整个管道，包括数据加载，参数初始化，模型学习，参数解释，直至数据插补和外推。 Mogptk实现了文献的主要多输出协方差内核以及基于光谱的参数初始化策略。可以在http://github.com/games-com/games-uchile/mogptk上找到以jupyter笔记本形式的源代码，教程和示例以及API文档。
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
