Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-17T08:19:35.341Z Has data issue: false hasContentIssue false

Runs in coin tossing: a general approach for deriving distributions for functionals

Published online by Cambridge University Press:  30 March 2016

Lars Holst*
Affiliation:
Royal Institute of Technology
Takis Konstantopoulos*
Affiliation:
Uppsala University
*
Postal address: Department of Mathematics, Royal Institute of Technology, SE-10044 Stockholm, Sweden. Email address: [email protected]
∗∗ Postal address: Department of Mathematics, Uppsala University, SE-75106 Uppsala, Sweden. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We take a fresh look at the classical problem of runs in a sequence of independent and identically distributed coin tosses and derive a general identity/recursion which can be used to compute (joint) distributions of functionals of run types. This generalizes and unifies already existing approaches. We give several examples, derive asymptotics, and pose some further questions.

Type
Research Papers
Copyright
Copyright © 2015 by the Applied Probability Trust 

References

Ahlfors, L. V. (1978). Complex Analysis. McGraw-Hill, New York.Google Scholar
Balakrishnan, N. and Koutras, M. V. (2002). Runs and Scans with Applications. John Wiley, New York.Google Scholar
Erdős, P. and Rényi, A. (1970). On a new law of large numbers. J. Analyse Math. 23 103-111.CrossRefGoogle Scholar
Erdős, P. and Révész, P. (1977). On the length of the longest headrun. In Topics in Information Theory (Colloq. Math. Soc. János Bolyai 16), North-Holland, Amsterdam, pp. 219-228.Google Scholar
Feller, W. (1968). An Introduction to Probability Theory and Its Applications, Vol. I, 3rd edn. John Wiley, New York.Google Scholar
Fu, J. C. and Lou, W. Y. W. (2003). Distribution Theory of Runs and Patterns and Its Applications. World Scientific, River Edge, NJ.CrossRefGoogle Scholar
Gordon, L., Schilling, M. F. and Waterman, M. S. (1986). An extreme value theory for long head runs. Prob. Theory Relat. Fields 72 279-287.CrossRefGoogle Scholar
Graham, R. L., Knuth, D. E. and Patashnik, O. (1994). Concrete Mathematics, 2nd edn. Addison-Wesley, Reading, MA.Google Scholar
Grimmett, G. R. and Stirzaker, D. R. (2001). Probability and Random Processes, 3rd edn. Oxford University Press.CrossRefGoogle Scholar
Guibas, L. J. and Odlyzko, A. M. (1980). Long repetitive patterns in random sequences. Z. Wahrscheinlichkeitsth. 53 241-262.CrossRefGoogle Scholar
Makri, F. S. and Psillakis, Z. M. (2011). On success runs of length exceeded a threshold. Methodol. Comput. Appl. Prob. 13 269-305.CrossRefGoogle Scholar
Murray, D. B. and Teare, S. W. (1993). Probability of a tossed coin landing on edge. Phys. Rev. E 48 2547-2552.CrossRefGoogle ScholarPubMed
Muselli, M. (1996). Simple expressions for success run distributions in Bernoulli trials. Statist. Prob. Lett. 31 121-128.CrossRefGoogle Scholar
Philippou, A. N. and Makri, F. S. (1986). Success runs and longest runs. Statist. Prob. Lett. 4 101-105, 211–215.CrossRefGoogle Scholar
Révész, P. (1980). Strong theorems on coin tossing. In Proceedings of the International Congress of Mathematicians Acad. Sci. Fennica, Helsinki, pp. 749754.Google Scholar
Von Mises, R. (1921). Zur theorie der iterationen. Z. Angew. Math. Mech. 1 298-307.Google Scholar
Von Mises, R. (1981). Probability, Statistics and Truth. Dover, New York.Google Scholar