The Annals of Probability

Large Deviation Results for a Class of Markov Chains Arising from Population Genetics

Gregory J. Morrow and Stanley Sawyer

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Let $\{X_n\}$ be a Markov chain on a bounded set in $R^d$ with $E_x(X_1) = f_N(x) = x + \beta_N h_N(x)$, where $x_0$ is a stable fixed point of $f_N(x) = x$, and $\operatorname{Cov}_x(X_1) \approx \sigma^2(x)/N$ in various senses. Let $D$ be an open set containing $x_0$, and assume $h_N(x) \rightarrow h(x)$ uniformly in $D$ and either $\beta_N \equiv 1$ or $\beta_N \rightarrow 0, \beta_N \gg \sqrt{\log N/N}$. Then, assuming various regularity conditions and $X_0 \in D$, the time the process takes to exit from $D$ is logarithmically equivalent in probability to $e^{VN\beta_N}$, where $V > 0$ is the solution of a variational problem of Freidlin-Wentzell type $\lbrack \text{if} \beta_N \rightarrow 0 \text{and} d = 1, V = \inf\{2 \int^y_{x_0}\sigma^{-2}(u)|h(u) du|: y \in \partial D\} \rbrack$. These results apply to the Wright-Fisher model in population genetics, where $\{X_n\}$ represent gene frequencies and the average effect of forces such as selection and mutation are much stronger than effects due to finite population size.

Article information

Ann. Probab., Volume 17, Number 3 (1989), 1124-1146.

First available in Project Euclid: 19 April 2007

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


Primary: 60F10: Large deviations
Secondary: 60J10: Markov chains (discrete-time Markov processes on discrete state spaces) 92A10 60G40: Stopping times; optimal stopping problems; gambling theory [See also 62L15, 91A60]

Large deviations Markov chains Ventsel-Freidlin Wright-Fisher population genetics


Morrow, Gregory J.; Sawyer, Stanley. Large Deviation Results for a Class of Markov Chains Arising from Population Genetics. Ann. Probab. 17 (1989), no. 3, 1124--1146. doi:10.1214/aop/1176991260.

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