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December 2020 Structured discrepancy in Bayesian model calibration for ChemCam on the Mars Curiosity rover
K. Sham Bhat, Kary Myers, Earl Lawrence, James Colgan, Elizabeth Judge
Ann. Appl. Stat. 14(4): 2020-2036 (December 2020). DOI: 10.1214/20-AOAS1373


The Mars rover Curiosity carries an instrument called ChemCam to determine the composition of the soil and rocks via laser-induced breakdown spectroscopy (LIBS). Los Alamos National Laboratory has developed a simulation capability that can predict spectra from ChemCam, but there are major-scale differences between the prediction and observation. This presents a challenge when using Bayesian model calibration to determine the unknown physical parameters that describe the LIBS observations. We present an analysis of LIBS data to support ChemCam based on including a structured discrepancy model in a Bayesian model-calibration scheme. This is both a novel application and an illustration of the importance of setting scientifically informed and constrained discrepancy models within Bayesian model calibration.


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K. Sham Bhat. Kary Myers. Earl Lawrence. James Colgan. Elizabeth Judge. "Structured discrepancy in Bayesian model calibration for ChemCam on the Mars Curiosity rover." Ann. Appl. Stat. 14 (4) 2020 - 2036, December 2020.


Received: 1 November 2019; Revised: 1 July 2020; Published: December 2020
First available in Project Euclid: 19 December 2020

MathSciNet: MR4194259
Digital Object Identifier: 10.1214/20-AOAS1373

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.14 • No. 4 • December 2020
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