Acta Oeconomica Pragensia 2012, 20(3):76-88 | DOI: 10.18267/j.aop.371

A Comparison of Classical and Bayesian Probability and Statitics (3)

Petr Hebák
Univerzita Hradec Králové, Fakulta informatiky a managementu; Vysoká škola ekonomická v Praze, Fakulta informatiky a statistiky (hebak@centrum.cz).

Statistics has been developing for almost 250 years - since the publication of an essay which included one theorem called Bayes' after the author. This whole period (since 1763 to this day) has been accompanied by a duel between the supporters of a subjective concept of probability and those who refuse everything but a purely objective concept of probability as well as statistics. While the 18th and 19th centuries accepted the importance of the subjective (let us say Bayesian) way of thinking for the development of probability and statistics without a problem, in the 20th century the classic (frequentist) way took over and has been dominant in teaching and textbooks to this day. Only in the second half of the 20th century did the situation begin to change slowly. Reasons for that are partly described in the present article, but arguments and simple examples supporting the Bayesian way in comparison with the classic one are clear and generally respected worldwide. Unsuspected new computing possibilities have caused an explosive development of Bayesian statistics, which has infiltrated almost all the areas of statistics and a number of other scientific fields. It is not possible to expect a retreat of the different philosophical or pedagogical positions of the fighting schools of thought (even though it is really needed), but the use of advantages of both the approaches is methodologically not only possible, but even expected. Part of the teaching of statistics must be prepared for these changes, but it has not been the case in the Czech Republic at all so far.

Keywords: subjective probability, frequentist statistics, classical and Bayesian approach and thinking, Bayes' theorem, point estimation, prior and posterior distribution, Bayesian Credible interval, hypothesis testing
JEL classification: C82, E21

Published: June 1, 2012  Show citation

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Hebák, P. (2012). A Comparison of Classical and Bayesian Probability and Statitics (3). Acta Oeconomica Pragensia20(3), 76-88. doi: 10.18267/j.aop.371
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References

  1. BAYARRI, M. J.; BERGER, J. O. The Interplay of Bayesian and Frequentist Analysis. Statistical Science. 2004, vol. 19, s. 58-80. Go to original source...
  2. BERGER, J. O.; SELKE, T Testing a Point Null Hypothesis: the Irreconcilability of p-Values and Evidence. Journal of the American Statistical Association. 1987, vol. 82, s. 112-122. Go to original source...
  3. BLACKWELL, D. Basic Statistics. Los Angeles : Mc-Graw Hill, 1969.
  4. BOLSTAD, W. M. Introduction to Bayesian Statistics. Hoboken : John Wiley and Sons, 2004. Go to original source...
  5. BREIMAN, L. Nail Finders. Proceedings of the Berkeley Conference in Honor of J. Neyman and J. Kiefer, Wadsworth, Belmont, California, 1985. Vol. 1, s. 201-212.
  6. CASELLA, G.; BERGER, L. R. Reconciling Bayesian and Frequentist Evidence in the One-Sided Testing Problem. Journal of the American Statistical Association, 1987, vol. 82, s. 106-135. Go to original source...
  7. EFRON, B. Large Scale Inference - Empirical Bayes Methods for Estimation, Testing and Prediction. Cambridge; New York : Cambridge University Press, 2010. Go to original source...
  8. FISHBURN, P C. The Axioms of Subjective Probability (with Discussion). Statistical Science. 1986, vol. 1, s. 335-358. Go to original source...
  9. FISHER, R. A. Statistical Methods for Research Workers. 14. ed. Edinburgh; New York : Hafner, Oliver and Boyd, 1925.
  10. HÁTLE, J.; LIKEŠ, J. Základy počtu pravděpodobnosti a matematické statistiky. Praha : SNTL, Alfa, 1972.
  11. HAWKING, S. W. A Brief History of Time. New York : Bantam Books, 1988. Go to original source...
  12. HEBÁK, P et al. Praktikum k výuce matematické statistiky II: Testování hypotéz. Praha : Vysoká škola ekonomická v Praze, 2000.
  13. JACKMAN, S. Bayesian Analysis for the Social Sciences. Chichester : John Wiley and Sons, 2010. Go to original source...
  14. KAHOUNOVÁ, J. Praktikum k výuce matematické statistiky I: Odhady. Praha : Vysoká škola ekonomická v Praze, 2000.
  15. LEHMANN, E. L. Testing Statistical Hypotheses. New York : John Wiley and Sons, 1959.
  16. LEHMANN, E. L. The Neyman Pearson Theory after 50 years. Proceedings of the Berkeley Conference in Honor of J. Neyman and J. Kiefer, Wadsworth, Belmont, California, 1985, vol. 1, s. 1-14.
  17. LINDLEY, D. V.; PHILLIPS, L. D. Inference for a Bernoulli Process (a Bayesian View). The American Statistician. 1976, vol. 30, s. 112-119. Go to original source...
  18. MORRISON, D. E.; HENKEL, R. E. The Significance Test Controversy. Chicago : Aldine, 1970.
  19. NEYMAN, J.; PEARSON, E. S. On the Testing of Statistical Hypotheses in Relation to Probability A Priori. Proc. of the Cambridge Phil. Soc. 1933, vol. 29, s. 492-510. Go to original source...
  20. NEYMAN, J. Contribution to the Theory of Sampling Human Populations. Journal of the American Statistical Association. 1938, vol. 33, s. 101-116. Go to original source...
  21. PEARSON, K. The Grammar of Science. London : Adam and Charles Black, 1892. Go to original source...
  22. POPPER, K. The Logic of Scientific Discovery. London; New York : Hutchinson; Harper Torchbooks, 1935; 1959; 1968.
  23. PRESS, S. J. Subjective and Objective Bayesian Statistics. New York : John Wiley and Sons, 2003. Go to original source...
  24. STUDENT (Wiliam Sealy Gosset). Paper on the Student t- distribution. Biometrika. 1908, vol. 6, s. 1-25. Go to original source...
  25. WALD, A. Contributions to the Theory of Statistical Estimation and Testing Hypotheses. Ann. Math. Statistics. 1939, vol. 10, s. 299-326. Go to original source...
  26. WANG, C. Sense and Nonsense of Statistical Inference. New York : Marcel Dekker, 1993.
  27. ZILIAK, S. T; MCCLOSKEY D. N. The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. Michigan : University Press, 2007. Go to original source...

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