Remarks on multivariate Gaussian Process

Zexun Chen, Jun Fan, Kuo Wang

Research output: Working paperPreprint

Abstract / Description of output

Gaussian processes occupy one of the leading places in modern statistics and probability theory due to their importance and a wealth of strong results. The common use of Gaussian processes is in connection with problems related to estimation, detection, and many statistical or machine learning models. With the fast development of Gaussian process applications, it is necessary to consolidate the fundamentals of vector-valued stochastic processes, in particular multivariate Gaussian processes, which is the essential theory for many applied problems with multiple correlated responses. In this paper, we propose a precise definition of multivariate Gaussian processes based on Gaussian measures on vector-valued function spaces, and provide an existence proof. In addition, several fundamental properties of multivariate Gaussian processes, such as strict stationarity and independence, are introduced. We further derive multivariate Brownian motion including It\^o lemma as a special case of a multivariate Gaussian process, and present a brief introduction to multivariate Gaussian process regression as a useful statistical learning method for multi-output prediction problems.
Original languageEnglish
Number of pages11
Publication statusPublished - 19 Oct 2020

Keywords / Materials (for Non-textual outputs)

  • math.ST
  • math.PR
  • stat.TH
  • Gaussian measure
  • Gaussian process
  • multivariate Gaussian process
  • multivariate Gaussian distribution
  • matrix-variate Gaussian distribution
  • pre-Brownian motion


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