Multivariate Gaussian Processes

Multivariate Gaussian Processes is a stochastic process that generalizes univariate Gaussian Processes by modeling multivariate functions as a joint Gaussian distribution over multiple output variables, characterized by a mean function and a matrix-valued covariance function encoding inter-variable and spatial-temporal dependencies.

458

Publications

29.7K

Citations

1.1K

Authors

533

Institutions

Publications per year

2017–2026

165

Authors

1.1K

  1. GL
    Georg Lindgren

    Statistics Sweden, Sweden

    6 publications · 188 citations

    6

    h-index

  2. RA
    Raed Al Kontar

    University of Michigan, United States

    4 publications · 124 citations

    4

    h-index

  3. YL
    Yongxiang Li

    Chinese Academy of Sciences, China

    3 publications · 77 citations

    3

    h-index

  4. TC
    Tao Chen

    University of Surrey, United Kingdom

    3 publications · 303 citations

    3

    h-index

  5. DA
    David A. Clifton

    University of Oxford, United Kingdom

    3 publications · 295 citations

    3

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 26 publications · 1.2K citations

    16

    h-index

  2. Technometrics

    7 publications · 336 citations

    8

    h-index

  3. IEEE Transactions on Signal Processing

    7 publications · 554 citations

    7

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 7 publications · 1.2K citations

    7

    h-index

  5. Signal Processing

    6 publications · 216 citations

    6

    h-index

1–5