Gaussian processes for time-series modelling

被引：424

作者：

Roberts, S. ^{[1
]}

Osborne, M. ^{[1
]}

Ebden, M. ^{[1
]}

Reece, S. ^{[1
]}

Gibson, N. ^{[2
]}

Aigrain, S. ^{[2
]}

机构：

[1] Univ Oxford, Dept Engn Sci, Oxford OX1 3PU, England

[2] Univ Oxford, Dept Astrophys, Oxford OX1 3PU, England

来源：

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | 2013年 / 371卷 / 1984期

基金：

英国工程与自然科学研究理事会;

关键词：

Gaussian processes; time-series analysis; Bayesian modelling;

D O I：

10.1098/rsta.2011.0550

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：25

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