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The Scope of Bayesian Reasoning
Published online by Cambridge University Press: 19 June 2023
Extract
There is one sense in which Bayes’ theorem, and its use in statistics and in scientific inference, is clearly uncontroversial. It is an authentic, certified, theorem of the probability calculus, and even the founders of classical statistical inference, Fisher, Neyman and Pearson, were explicit about seeing no difficulty in the use of Bayes’ theorem when the conditions for its application were satisfied. For example, Fisher writes, “When there really is exact knowledge a priori Bayes’ method is available” (1971, p. 194).
- Type
- Part V: Bayesian Philosophy of Science
- Information
- Copyright
- Copyright © 1993 by the Philosophy of Science Association
Footnotes
1
Acknowledgment for support of research is due to the National Science Foundation.
References
Carnap, R. (1971), “A Basic System of Inductive Logic, Part 1”, in Studies in lnductive Logic and Probability I, Carnap and Jeffrey (eds.). Berkeley: University of Califomia Press, pp. 33-165.CrossRefGoogle Scholar
Carnap, R. (1950), The Logical Foundations of Probability. Chicago: University of Chicago Press.Google Scholar
Cooley, J. C. (1959), “Toulmin's Revolution In Logic,” Journal of Philosophy 56: 297-319.CrossRefGoogle Scholar
de Finetti, B. (1937), “La Prevision: Ses Lois Logiques, Ses Sources Sujectives”, Annales De L’ Institute Henri Poincare 7.Google Scholar
Fisher, R. A. (1956), Statistical Methods and Scientiftc lnference. New York: Hafner Publishing Co.Google Scholar
Fisher, R. A. (1930), “Inverse Probability”, Proceedings of the Cambridge Philosophical Society 26: 528-535.CrossRefGoogle Scholar
Fisher, R. A. (1924), “On a Distribution Yielding the Error Functions of Several Weil Known Statistics”, Proceedings of the International Mathematical Mathematical Congress, Toronto. pp. 805-812.Google Scholar
Fisher, R. A. The Design of Experiments, Hafner, New York, 1971. Probability Statistics Statistics First Edition 1935Google Scholar
Hintikka, J. (1966), “A Two-Dimensional Continuum of Inductive Methods”, in Aspects of Inductive Logic, Hintikka and Suppes (Eds). Amsterdam: North Holland, pp. 113-132.CrossRefGoogle Scholar
Howson, C. and Urbach, P. (1989), Scientific Reasoning: the Bayesian Approach. La Salle, Ill: Open Court.Google Scholar
Jaynes, E. T. (1959), Probability Theory in Science and Engineering; Colloquium Lectures in Pure and Applied Science 4, 1958: Dallas, Texas, Socony Mobile Oil Corp. pp. 152-187.Google Scholar
Kyburg, H. and Pittarelli, M. (1992), “Some Problems for Convex Bayesians,” UAJ-92, Proceedings, pp. 149-154.CrossRefGoogle Scholar
Levi, I. (1974), “On Indeterminate Probabilities”, Journal of Philosophy 71: 39-418.CrossRefGoogle Scholar
Lewis, D. K. (1980) “A Subjectivist's Guide To Objective Chance”, Studies in Inductive Logic and Probability II, Jeffrey (Ed). Berkeley and Los Angeles: University of Califomia Press, pp. 263-293.Google Scholar
Miller, D. (1966), “A Paradox of Information,” British Journal for the Philosophy of Science 17: 59-61.Google Scholar
Russell, B. (1948), Human Knowledge, Its Scope and Limits. New York: Simon and Schuster.Google Scholar
Ryle, G. (1937), “Induction and Hypothesis”, Proceedings of the Aristotelian Society, Supplementary Volume 16: 36-62.Google Scholar
Ryle, G. (1957), “Predicting and Inferring”, in The Colston Papers 9, Korner (ed), pp. 165-170.Google Scholar
Savage, L. J. (1962),“Subjective Probability and Statistical Practise”, Foundations of Statistical Inference, Bamard and Cox (Eds). New York: John Wiley and Sons, pp. 9-35.Google Scholar
Strawson, Peter F. (1952), Introduction to Logical Theory, London and New York: Methuen and Co.Google Scholar
Toulmin, S. (1961), Foresight and Understanding. Bloomington: Indiana University Press.Google Scholar