Quasi-stationary distributions for a class of discrete-time Markov chains
Pauline Coolen‐Schrijner, Erik A. van Doorn
Abstract
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Pauline Coolen‐Schrijner, Erik A. van Doorn
Abstract
Open-access reader
This paper is concerned with the circumstances under which a discrete-time absorbing Markov chain has a quasi-stationary distribution. We showed in a previous paper that a pure birth-death process with an absorbing bot-tom state has a quasi-stationary distribution – actually an infinite family of quasi-stationary distributions – if and only if absorption is certain and the chain is geometrically transient. If we widen the setting by allowing absorption in one step (killing) from any state, the two conditions are still necessary, but no longer sufficient. We show that the birth-death-type of behaviour prevails as long as the number of states in which killing can oc-cur is finite. But if there are infinitely many such states, and if the chain is geometrically transient and absorption certain, then there may be 0, 1, or infinitely many quasi-stationary distributions. Examples of each type of behaviour are presented. We also survey and supplement the theory of quasi-stationary distributions for discrete-time Markov chains in general.
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This paper is concerned with the circumstances under which a discrete-time absorbing Markov chain has a quasi-stationary distribution. We showed in a previous paper that a pure birth-death process with an absorbing bot-tom state has a quasi-stationary distribution – actually an infinite family of quasi-stationary distributions – if and only if absorption is certain and the chain is geometrically transient. If we widen the setting by allowing absorption in one step (killing) from any state, the two conditions are still necessary, but no longer sufficient. We show that the birth-death-type of behaviour prevails as long as the number of states in which killing can oc-cur is finite. But if there are infinitely many such states, and if the chain is geometrically transient and absorption certain, then there may be 0, 1, or infinitely many quasi-stationary distributions. Examples of each type of behaviour are presented. We also survey and supplement the theory of quasi-stationary distributions for discrete-time Markov chains in general.
Key concepts: Markov chain, Stationary distribution, Discrete phase-type distribution, Stationary state, Discrete time and continuous time, Type (biology), Mathematics, Statistical physics