Gaussian Process Sampling

Gaussian Process Sampling is a stochastic simulation method that generates realizations from a Gaussian process prior, distinguished by its specification of a mean function and covariance kernel to produce sample paths consistent with the probabilistic structure of Gaussian random functions.

98

Publications

4.9K

Citations

323

Authors

169

Institutions

Publications per year

2017–2026

58

Authors

323

  1. LS
    Laura Swiler

    Sandia National Laboratories, United States

    3 publications · 1.2K citations

    3

    h-index

  2. MB
    Mona Buisson-Fenet

    RWTH Aachen University, Germany

    2 publications · 39 citations

    2

    h-index

  3. ZH
    Zhaolin Hu

    Tongji University, China

    2 publications · 88 citations

    2

    h-index

  4. LS
    Lihua Sun

    Tongji University, China

    2 publications · 88 citations

    2

    h-index

  5. PK
    Pradeep K. Khosla

    Carnegie Mellon University, United States

    2 publications · 66 citations

    2

    h-index

1–5 of 323

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 138 citations

    6

    h-index

  2. Neural Information Processing Systems

    Conference · 2 publications · 109 citations

    2

    h-index

  3. Technometrics

    2 publications · 37 citations

    2

    h-index

  4. 2

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 2 publications · 68 citations

    2

    h-index

1–5