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Random-allocation and urn models

Published online by Cambridge University Press:  14 July 2016

J. Gani*
Affiliation:
Centre for Mathematics and Its Applications, Mathematical Sciences Institute, Australian National University, Canberra, ACT 0200, Australia. Email address: [email protected]

Abstract

We review some urn and random-allocation models, mostly using probability generating function (PGF) methods. We begin by formulating a basic problem which can be thought of as either an urn or a random-allocation model; a PGF solution to it is outlined. When the compartments in the latter model are no longer homogeneous, the multivariate PGF can still be derived, though the algebra becomes cumbersome. Some results are given for the case where there are two types of compartment and for the case where there are two types of ball. Some comments are offered on the Frobenius–Harper property of PGFs.

Type
Part 6. Stochastic processes
Copyright
Copyright © Applied Probability Trust 2004 

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References

Feller, W. (1968). An Introduction to Probability Theory and Its Applications , Vol. 1, 3rd edn. John Wiley, New York.Google Scholar
Frobenius, G. (1910). Über die Bernoulli'schen Zahlen und die Eulerschen Polynome. Sitzungberichte Preuss. Akad. Wissensch. 1910, 829830.Google Scholar
Gani, J. (1991). Generating function methods in a random allocation problem of epidemics. Bull. Inst. Combinatorics Appl. 3, 4350.Google Scholar
Gani, J. (1993). Random allocation methods in an epidemic model. In Stochastic Processes , eds Cambanis, S., Ghosh, J. K., Karandikar, R. L. and Sen, P. K., Springer, New York, pp. 97106.Google Scholar
Gani, J. (2002a). Needle sharing infections among heterogeneous IVDUs. Monatsh. Math. 135, 2536.Google Scholar
Gani, J. (2002b). Nonhomogeneous susceptibles and infectives among needle sharing IVDUs. In Advances in Statistics, Combinatorics and Related Areas , eds Gulati, C., Lin, Y.-X., Mishra, S. and Rayner, J., World Scientific, Singapore, pp. 9199.Google Scholar
Gani, J. (2003). Two types of infectives among homogeneous IVDU susceptibles. In Mathematical Statistics and Applications (IMS Lecture Notes-Monogr. Ser. 42), eds Moore, M., Froda, S. and Leger, C., Institute of Mathematical Statistics, Bethesda, MD, pp. 281290.CrossRefGoogle Scholar
Gani, J. and Yakowitz, S. (1993). Modelling the spread of HIV among intravenous drug users. IMA J. Math. Appl. Med. Biol. 10, 5165.Google Scholar
Harper, L. H. (1967). Stirling behaviour is asymptotically normal. Ann. Math. Statist. 38, 410414.Google Scholar
Heyde, C. C. and Schuh, H.-J. (1978). Uniform bounding of probability generating functions and the evolution of reproduction rates in birds. J. Appl. Prob. 15, 243250.Google Scholar
Johnson, N. L., Kotz, S. and Kemp, A. W. (1993). Univariate Discrete Distributions , 2nd edn. John Wiley, New York.Google Scholar
Rutherford, R. D. (1954). On a contagious distribution. Ann. Math. Statist. 25, 703713.Google Scholar
Warren, D. (1999). The Frobenius-Harper technique in a general recurrence model. J. Appl. Prob. 36, 3047.Google Scholar
Warren, D. and Seneta, E. (1996). Peaks and Eulerian numbers in a random sequence. J. Appl. Prob. 33, 101114.Google Scholar
Woodbury, M. A. (1949). On a probability distribution. Ann. Math. Statist. 20, 311313.Google Scholar