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Introducing BACCO, an R Bundle for Bayesian Analysis of Computer Code Output

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  • Hankin, Robin K. S.

Abstract

This paper introduces the BACCO bundle of R routines for carrying out Bayesian analysis of computer code output. The bundle comprises packages emulator and calibrator, computerized implementations of the ideas of Oakley and O'Hagan (2002) and Kennedy and O'Hagan (2001a) respectively. The bundle is self-contained and fully documented R code, and includes a toy dataset that furnishes a working example of the functions. Package emulator carries out Bayesian emulation of computer code output; package calibrator allows the incorporation of observational data into model calibration using Bayesian techniques. The package is then applied to a dataset taken from climate science.

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  • Hankin, Robin K. S., 2005. "Introducing BACCO, an R Bundle for Bayesian Analysis of Computer Code Output," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i16).
    • Handle: RePEc:jss:jstsof:v:014:i16
    DOI: http://hdl.handle.net/10.18637/jss.v014.i16
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    Cited by:

    1. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    2. Zhu, Chuanqi & Tian, Wei & Yin, Baoquan & Li, Zhanyong & Shi, Jiaxin, 2020. "Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms," Applied Energy, Elsevier, vol. 268(C).
    3. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    4. Jackson Samuel E. & Vernon Ian & Liu Junli & Lindsey Keith, 2020. "Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-33, April.
    5. Palomo, Jesús & Paulo, Rui & García-Donato, Gonzalo, 2015. "SAVE: An R Package for the Statistical Analysis of Computer Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i13).
    6. Garbuno-Inigo, A. & DiazDelaO, F.A. & Zuev, K.M., 2016. "Gaussian process hyper-parameter estimation using Parallel Asymptotically Independent Markov Sampling," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 367-383.
    7. Soetaert, Karline & Petzoldt, Thomas, 2010. "Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i03).
    8. repec:jss:jstsof:46:i08 is not listed on IDEAS
    9. Topping, Christopher John & Høye, Toke Thomas & Odderskær, Peter & Aebischer, Nicholas J., 2010. "A pattern-oriented modelling approach to simulating populations of grey partridge," Ecological Modelling, Elsevier, vol. 221(5), pages 729-737.
    10. repec:jss:jstsof:33:i03 is not listed on IDEAS
    11. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).

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