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Empirical Bayes as a Tool

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Mathematics as a Tool

Part of the book series: Boston Studies in the Philosophy and History of Science ((BSPS,volume 327))

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Abstract

Bayesian methods are currently underdoing a deep transformation due to the use of computing power. The aim of this chapter is to analyze this transformation by examining a specific example: the use of Baysian methods in climate science.

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Notes

  1. 1.

    This report has triggered a harsh debate within the philosophy of science community: cf. Frigg et al. (2014) and Winsberg and Goodwin (2016).

References

  • Annan, J. D., & Hargreaves, J. C. (2006). Using multiple observationally-based constraints to estimate climate sensitivity. Geophys Res Lett, 33, L06704.

    Article  Google Scholar 

  • Berliner, L. M., Richard, L. M., Levine, A., & Shea, D. S. (2000). Bayesian climate change assessment. Journal of Climate, 13, 3805–3820.

    Article  Google Scholar 

  • Casella, G. (1992). Ilustrating empirical Bayes methods. Chemometrics and Intelligent Laboratory Sytems, 16, 107–125.

    Article  CAS  Google Scholar 

  • Frigg, R., Bradley, S., Du, H., & Smith, L. A. (2014). Model Error and Ensemble Forecasting: A Cautionary Tale. Iin: Guichun C. Guo and Chuang Liu (eds.) Scientific Explanation and Methodology of Science. Singapore: World Scientific, 2014, 58–66.

    Google Scholar 

  • Hasselmann, K. (1998). Conventional and Bayesian approach to climate-change detection and attribution. Quarterly Journal of the Royal Meteorological Society, 124, 2541–2565.

    Article  Google Scholar 

  • Hegerl, G. C., Crowley, T. J., Hyde, W. T., & Frame, D. J. (2006). Climate sensitivity constrained by temperature reconstructions over the past seven centuries. Nature, 440, 1029–1032.

    Article  CAS  Google Scholar 

  • Kass, R. (2011). Statistical inference: The big picture. Statistical Science, 26, 1–20.

    Article  Google Scholar 

  • Kennedy, M. C., & O’Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society Series B, 3, 425–464.

    Article  Google Scholar 

  • Mayo D. G. (2013). Discussion: Bayesian methods: Applied? Yes. Philosophical Defense? In Flux. The American Statistician 67(1): 11–15. (Commentary on A. Gelman and C. Robert “Not only defended but also applied: The perceived absurdity of Bayesian inference” (with discussion)

  • Parker, D. (2010). Whose probabilities? Predicting climate change with ensembles of models. Philosophy of Science, 77(5), 985–997.

    Article  Google Scholar 

  • Rober C. (2016). Des spécificités de l’approche bayésienne et de ses justifications en statistique inférentielle. In Drouet (Ed.), Eclairages philosophiques sur les méthodes bayésiennes, Editions Matériologiques, Paris, to appear.

    Google Scholar 

  • Rougier, J. C. (2007). Probabilistic inference for future climate using an ensemble of climate model evaluations. Climatic Change, 81, 247–264.

    Article  Google Scholar 

  • Rougier, J. C., & Crucifix, M. (2012). Uncertainty in climate science and climate policy. In L. Lloyd, & E. Winsberg (Eds.), Conceptual issues in climate modeling. University of Chicago Press (forthcoming).

    Google Scholar 

  • van Fraassen, B. (2008). Scientific representation: Paradoxes of perspective. Oxford: Clarendon Press, Oxford.

    Book  Google Scholar 

  • Winsberg, E., & Goodwin, W. M. (2016). The adventures of climate science in the sweet land of idle arguments. Studies in History and Philosophy of Modern Physics, 54, 9–17.

    Google Scholar 

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Correspondence to Anouk Barberousse .

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Barberousse, A. (2017). Empirical Bayes as a Tool. In: Lenhard, J., Carrier, M. (eds) Mathematics as a Tool. Boston Studies in the Philosophy and History of Science, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-54469-4_9

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