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An extension of a convergence theorem for Markov chains arising in population genetics

Part of: Markov processes Limit theorems

Published online by Cambridge University Press:  24 October 2016

Martin Möhle  and
Morihiro Notohara
Martin Möhle*
Affiliation:
Eberhard Karls Universität Tübingen
Morihiro Notohara*
Affiliation:
Nagoya City University
*
* Postal address: Mathematisches Institut, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany. Email address: [email protected]
** Postal address: Graduate School of Natural Sciences, Nagoya City University, Mizuho, Nagoya, 467-8501, Japan. Email address: [email protected]
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Abstract

An extension of a convergence theorem for sequences of Markov chains is derived. For every positive integer N let (X N (r))r be a Markov chain with the same finite state space S and transition matrix ΠN =I+d N B N , where I is the unit matrix, Q a generator matrix, (B N )N a sequence of matrices, limN℩∞ c N = limN→∞d N =0 and limN→∞ c N ∕d N =0. Suppose that the limits P≔limm→∞(I+d N Q)m and G≔limN→∞ P B N P exist. If the sequence of initial distributions P X N (0) converges weakly to some probability measure μ, then the finite-dimensional distributions of (X N ([t∕c N ))t≥0 converge to those of the Markov process (X t )t≥0 with initial distribution μ, transition matrix PetG and limN→∞(I+d N Q+c N B N )[t∕c N]

Keywords

ConvergenceMarkov chainpopulation geneticsseparation of time-scales

MSC classification

Primary: 60F05: Central limit and other weak theorems 92D10: Genetics
Secondary: 60J27: Continuous-time Markov processes on discrete state spaces 92D25: Population dynamics (general)
Type
Research Papers
Information
Journal of Applied Probability , Volume 53 , Issue 3 , September 2016 , pp. 953 - 956
Copyright
Copyright © Applied Probability Trust 2016 

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