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

James Gattiker (), Kary Myers (), Brian J. Williams (), Dave Higdon (), Marcos Carzolio () and Andrew Hoegh ()
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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

Chapter 55 in Handbook of Uncertainty Quantification, 2017, pp 1867-1907 from Springer

Abstract: 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.

Keywords: Bayesian analysis; Design and analysis of computer experiments; Gaussian process; Markov chain Monte Carlo; Statistical analysis of computer models (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-12385-1_58

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