References
- 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.
- Berger, J. O. (2000). Bayesian analysis: A look at today and thoughts of tomorrow, Journal of the American Statistical Association, 95, 1269-1276.
- Bernardo, J. M. (1979). Reference posterior distributions for Bayesian inference, Journal of the Royal Statistical Society: Series B(Statistical Methodological), 113-147.
- 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.
- De Finetti, B. (1938). Sur la condition d'equivalence partielle, Actualites Scientifiques et Industrielles, 739.
- Efron, B. (2009). The future of statistics, Amstat News, 363, 47-50.
- 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
- Gillies, D. (2000). Philosophical Theories of Probability, Psychology Press.
- Hume, D. (1748). Philosophical Essays Concerning Human Understanding, Andrew Millar.
- Jeffreys, H. (1939). Theory of Probability.
- 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
- Laplace, P. (1774). Memoire sur la probabilite des causes par les evenements, l'Academie Royale des Sciences, 6, 621-656.
- Lindley, D. V. (1965). Introduction to probability and statistics from Bayesian viewpoint, Part 2 inference, CUP Archive.
- 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.
- 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.
- 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
- Savage, L. J. (1954). The Foundations of Statistics, John Wiley and Sons, Inc.
- 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
- 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
- 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
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