응용통계연구 (The Korean Journal of Applied Statistics)

  • 제27권6호
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  • Pages.855-863
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  • 2014
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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베이지안 통계의 역사와 미래에 대한 조망

History and Future of Bayesian Statistics

  • 투고 : 2014.10.27
  • 심사 : 2014.12.05
  • 발행 : 2014.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

최근 계산 기술의 진보로 인하여, 베이지안 통계는 급속도로 확산되어 가고 있다. 그러나, 정보화 시대에 들어서면서 베이지안 통계를 비롯한 통계학은 새로운 문제들에 직면하게 되었다. 이 논문에서는 베이지안 통계의 역사를 간단히 살펴보고, 베이지안 통계의 현재의 영향력에 대해서 알아본다. 그리고 통계학의 미래와 통계학계가 직면한 도전과제들에 대하여 생각해 볼 것이다.

The recent computational revolution of Bayesian statistics has expanded use of the Bayesian statistics significantly; however, Bayesian statistics face a new set of challenges in the era of information technology. We survey the history of Bayesian statistics briefly and its expansion in the modern times. We then take a prospective future view of statistics and list challenges that the statistics community faces.

키워드

참고문헌

  1. Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. by the late Rev. Mr. Bayes, FRS. communicated by Mr. Price, in a letter to John Canton, AMFRS, Philosophical Trans- actions (1683-1775), 370-418.
  2. Berger, J. O. (2000). Bayesian analysis: A look at today and thoughts of tomorrow, Journal of the American Statistical Association, 95, 1269-1276.
  3. Bernardo, J. M. (1979). Reference posterior distributions for Bayesian inference, Journal of the Royal Statistical Society: Series B(Statistical Methodological), 113-147.
  4. Buhlmann, P., Carroll, R., Murphy, S., Roberts, G., Scott, M., Tavare, S., Triggs, C., Wang, J. L., Wasserstein, R. L., Madigan, D., Bartlett, P. and Zuma, K. (2014). Statistics and science: A report of the london workshop on the future of the statistical sciences.
  5. De Finetti, B. (1938). Sur la condition d'equivalence partielle, Actualites Scientifiques et Industrielles, 739.
  6. Efron, B. (2009). The future of statistics, Amstat News, 363, 47-50.
  7. Gelfand, A. E. and Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association, 85, 398-409. https://doi.org/10.1080/01621459.1990.10476213
  8. Gillies, D. (2000). Philosophical Theories of Probability, Psychology Press.
  9. Hume, D. (1748). Philosophical Essays Concerning Human Understanding, Andrew Millar.
  10. Jeffreys, H. (1939). Theory of Probability.
  11. Kleiner, A., Talwalkar, A., Sarkar, P. and Jordan, M. I. (2014). A scalable bootstrap for massive data, Journal of the Royal Statistical Society: Series B(Statistical Methodology), 76, 795-816. https://doi.org/10.1111/rssb.12050
  12. Laplace, P. (1774). Memoire sur la probabilite des causes par les evenements, l'Academie Royale des Sciences, 6, 621-656.
  13. Lindley, D. V. (1965). Introduction to probability and statistics from Bayesian viewpoint, Part 2 inference, CUP Archive.
  14. McGrayne, S. B. (2011). The Theory that Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, & Emerged Triumphant from Two Centuries of Controversy, Yale University Press.
  15. Minsker, S., Srivastava, S., Lin, L. and Dunson, D. B. (2014). Scalable and robust Bayesian inference via the median posterior, In Proceedings of the 31st International Conference on Machine Learning (ICML-14), 1656-1664.
  16. Ryan, T. P. and Woodall, W. H. (2005). The most-cited statistical papers, Journal of Applied Statistics, 32, 461-474. https://doi.org/10.1080/02664760500079373
  17. Savage, L. J. (1954). The Foundations of Statistics, John Wiley and Sons, Inc.
  18. Suchard, M. A., Wang, Q., Chan, C., Frelinger, J., Cron, A. and West, M. (2010). Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures, Journal of Computational and Graphical Statistics, 19, 419-438. https://doi.org/10.1198/jcgs.2010.10016
  19. Wrinch, D. and Jeffreys, H. (1919). On some aspects of the theory of probability, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 38, 715-731. https://doi.org/10.1080/14786441208636005
  20. Zhu, B. and Dunson, D. B. (2013). Locally adaptive Bayes nonparametric regression via nested Gaussian processes, Journal of the American Statistical Association, 108, 1445-1456. https://doi.org/10.1080/01621459.2013.838568

피인용 문헌

  1. Review of Mixed-Effect Models vol.28, pp.2, 2015, https://doi.org/10.5351/KJAS.2015.28.2.123