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Authors and Citation

Authors

Citation

Source: inst/CITATION

Ming, D. and Guillas, S. (2021) Linked Gaussian process emulation for systems of computer models using Matérn kernels and adaptive design, SIAM/ASA Journal on Uncertainty Quantification. 9(4), 1615-1642.

@Article{,
  title = {Linked Gaussian process emulation for systems of computer models using Matérn kernels and adaptive design},
  author = {Deyu Ming and Serge Guillas},
  journal = {SIAM/ASA Journal on Uncertainty Quantification},
  year = {2021},
  volume = {9},
  number = {4},
  pages = {1615--1642},
}

Ming, D., Williamson, D., and Guillas, S. (2023) Deep Gaussian process emulation using stochastic imputation, Technometrics. (65)2, 150-161.

@Article{,
  title = {Deep Gaussian process emulation using stochastic imputation},
  author = {Deyu Ming and Daniel Williamson and Serge Guillas},
  journal = {Technometrics},
  year = {2023},
  volume = {65},
  number = {2},
  pages = {150--161},
}

Ming, D. and Williamson, D. (2023) Linked deep Gaussian process emulation for model networks, arXiv:2306.01212.

@Unpublished{,
  title = {Linked deep Gaussian process emulation for model networks},
  author = {Deyu Ming and Daniel Williamson},
  note = {arXiv:2306.01212},
  year = {2023},
}

Ming, D. and Williamson, D. (2024) dgpsi: An R package powered by Python for modelling linked deep Gaussian processes, R package version 2.4.0. https://CRAN.R-project.org/package=dgpsi.

@Manual{,
  title = {dgpsi: An R package powered by Python for modelling linked deep Gaussian processes},
  author = {Deyu Ming and Daniel Williamson},
  note = {R package version 2.4.0},
  url = {https://CRAN.R-project.org/package=dgpsi},
  year = {2024},
}