Gaussian processes for time-series modelling | Zendy

Stephen Roberts | Zendy; Michael A. Osborne | Zendy; Mark Ebden | Zendy; Steven Reece | Zendy; Neale P. Gibson | Zendy; S. Aigrain | Zendy

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Gaussian processes for time-series modelling

Author(s) -

Stephen Roberts,

Michael A. Osborne,

Mark Ebden,

Steven Reece,

Neale P. Gibson,

S. Aigrain

Publication year - 2013

Publication title -

philosophical transactions of the royal society a mathematical physical and engineering sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.074

H-Index - 169

eISSN - 1471-2962

pISSN - 1364-503X

DOI - 10.1098/rsta.2011.0550

Subject(s) - gaussian process , computer science , series (stratigraphy) , gaussian , time series , bayesian probability , parametric statistics , algorithm , data mining , artificial intelligence , statistical physics , machine learning , mathematics , statistics , physics , paleontology , quantum mechanics , biology

In this paper, we offer a gentle introduction to Gaussian processes for time-series data analysis. The conceptual framework of Bayesian modelling for time-series data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes. We discuss how domain knowledge influences design of the Gaussian process models and provide case examples to highlight the approaches

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