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Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA

In: Handbook of Uncertainty Quantification

Author

Listed:
  • James Gattiker

    (Los Alamos National Laboratory, Statistical Sciences Group)

  • Kary Myers

    (Los Alamos National Laboratory, Statistical Sciences Group)

  • Brian J. Williams

    (Los Alamos National Laboratory, Statistical Sciences Group)

  • Dave Higdon

    (Virginia Bioinformatics Institute Virginia Tech, Social Decision Analytics Laboratory)

  • Marcos Carzolio

    (Virginia Tech, Department of Statistics)

  • Andrew Hoegh

    (Virginia Tech, Department of Statistics)

Abstract

The Gaussian Process Models for Simulation Analysis (GPMSA) Gaussian Process Models for Simulation Analysis (GPMSA) package is a set of functions written in the Matlab programming language aimed at emulating a computer model of a system being studied, calibrating this computer model to observations of the system, and giving predictions of the expected system response. Collectively, these capabilities comprise uncertainty quantification (UQ) in model-supported inference. This chapter will first discuss some background and motivation for the GPMSA code, then demonstrate the code’s function interfaces in the context of a series of illustrative example problems.

Suggested Citation

  • James Gattiker & Kary Myers & Brian J. Williams & Dave Higdon & Marcos Carzolio & Andrew Hoegh, 2017. "Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA," Springer Books, in: Roger Ghanem & David Higdon & Houman Owhadi (ed.), Handbook of Uncertainty Quantification, chapter 55, pages 1867-1907, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-12385-1_58
    DOI: 10.1007/978-3-319-12385-1_58
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