Abstract
We analyze quasi-stationary distributions $\mu^\epsilon$ of a family of
Markov chains $\X^\epsilon\_\epsilon>0$ that are random perturbations of a
bounded, continuous map $F:MM$ where $M$ is a subset of $\R^k$. Consistent
with many models in biology, these Markov chains have a closed absorbing set
$M_0M$ such that $F(M_0)=M_0$ and $F(MM_0)=MM_0$.
Under some large deviations assumptions on the random perturbations, we show
that if there exists a positive attractor for $F$ (i.e. an attractor for $F$ in
$MM_0$), then the weak* limit points of $\mu_\epsilon$ are supported
by the positive attractors of $F$. To illustrate the broad applicability of
these results, we apply them to nonlinear branching process models of
mutation-selection balance, nonequilibrium population dynamics, and
evolutionary games.
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