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

  • James Gattiker
  • Kary Myers
  • Brian Williams
  • Dave Higdon
  • Marcos Carzolio
  • Andrew Hoegh
Living reference work entry

Abstract

The 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.

Keywords

Bayesian analysis Design and analysis of computer experiments Gaussian process Markov chain Monte Carlo Statistical analysis of computer models 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • James Gattiker
    • 1
  • Kary Myers
    • 2
  • Brian Williams
    • 3
  • Dave Higdon
    • 4
  • Marcos Carzolio
    • 5
  • Andrew Hoegh
    • 6
  1. 1.Statistical Sciences GroupLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Statistical Sciences GroupLos Alamos National LaboratoryLos AlamosUSA
  3. 3.Statistical Sciences GroupLos Alamos National LaboratoryLos AlamosUSA
  4. 4.Social Decision Analytics LaboratoryVirginia Bioinformatics Institute Virginia TechArlingtonUSA
  5. 5.Department of StatisticsVirginia TechBlacksburgUSA
  6. 6.Department of StatisticsVirginia TechBlacksburgUSA

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