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A Limiting Frequency Approach to Probability Based on the Weak Law of Large Numbers

Published online by Cambridge University Press:  01 April 2022

Richard E. Neapolitan*
Affiliation:
Department of Computer Science, Northeastern Illinois University

Abstract

Von Mises defined a “physical” probability as a strict limit of the relative frequency of occurrence of an event in repeated trials. As a result of a number of criticisms of von Mises's approach, the more favored approach became the “propensity” interpretation. It is argued here that this interpretation is not compelling and that the only problem in von Mises's approach is the assumption that the relative frequency converges in a strict sense. This problem is then remedied by deducing the axioms of probability theory from the assumption that the relative frequency converges only in the sense of the weak law of large numbers.

Type
Research Article
Copyright
Copyright © 1992 by the Philosophy of Science Association

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Footnotes

Send reprint requests to the author, Department of Computer Science, Northeastern Illinois University, Chicago, IL 60625, USA.

References

Ash, R. B. (1970), Basic Probability Theory. New York: Wiley.Google Scholar
Church, A. (1940), “On the Concept of a Random Sequence”, Bulletin of the American Mathematical Society 36: 130135.CrossRefGoogle Scholar
de Finetti, B. (1972), Probability, Induction and Statistics: The Art of Guessing. New York: Wiley.Google Scholar
Geneva, Conference. (1937), “Colloque Consacré au Calcul des Probabilités”, Proceedings of a Conference at the Université de Genéve in 1937. The papers concerning the foundations of probability were published in the series Actualités Scientifiques et Industrielles, vol. 735, Hermann, 1938.Google Scholar
Good, I. J. (1965), The Estimation of Probabilities: An Essay on Modern Bayesian Methods. Research Monograph No. 30. Cambridge: M.I.T. Press.Google Scholar
Iversen, G. R.; Longcor, W. H.; Mosteller, F.; Gilbert, J. P.; and Youtz, C. (1971), “Bias and Runs in Dice Throwing and Recording: A Few Million Throws”, Psychometrika 36: 119.CrossRefGoogle Scholar
Kerrich, J. E. (1946), An Experimental Introduction to the Theory of Probability. Copenhagen: Einer Munksgaard.Google Scholar
Kolmogorov, A. N. (1929), “Das Gesetz der geiterierten Logarithmus”, Mathematishe Annalen 101: 126135.CrossRefGoogle Scholar
Kolmogorov, A. N. ([1933] 1956), Foundations of the Theory of Probability. Trans. New York: Chelsea. (Originally published as Grundbegriffe der Wahscheinlichkeitsrechnung. Berlin: Springer.)Google Scholar
Kolmogorov, A. N. (1983), “Combinatorial Basis of Information Theory and Probability Theory”, Russian Mathematical Surveys 38: 2940.CrossRefGoogle Scholar
Lambalgen, M. van (1987), Random Sequences. Ph.D. Thesis. University of Amsterdam.Google Scholar
Li, M. and Vitanyi, P. (1988), “Two Decades of Applied Kolmogorov Complexity. In Memoriam. A. N. Kolmogorov 1903–1987”, Proceedings of the 3rd IEEE Conference on Structure in Complexity Theory: 80101.Google Scholar
Lindley, D. V. (1965), Introduction to Probability and Statistics. Cambridge, England: The University Press.CrossRefGoogle Scholar
Marbe, K. (1916), Die Gleichformigkeit in der Welt, Untersuchungen zur Philosophie und Positiven Wissenschaft. Munich: C. H. Beck'sche Verlagsbuchhandlung Oskar Beck.Google Scholar
Mises, R. von (1919), “Grundlagen der Wahrscheinlichkeitsrechnung”, Mathematische Zeitschrift 5: 5299.CrossRefGoogle Scholar
Mises, R. von ([1928] 1957), Probability, Statistics, and Truth. Trans. London: Allen & Unwin. (Originally published as Wahrscheinlichkeit, Statistik, und Wahrheit. Berlin: Springer.)Google Scholar
Neapolitan, R. E. (1990), Probabilistic Reasoning in Expert Systems: Theory and Algorithms. New York: Wiley.Google Scholar
Popper, K. R. ([1935] 1959), Logic of Scientific Discovery. Trans. New York: Basic Books. (Originally published as Logik der Forschung. Berlin: Springer.)Google Scholar
Savage, L. J. (1954), The Foundations of Statistics. New York: Wiley.Google Scholar
Wald, A. (1936), “Die Widerspruchsfreiheit des Kollectivbegriffes der Wahrscheinlichkeitsrechnung”, Ergebnisse Eines Mathematischen Kolloquiums 8: 3872.Google Scholar