Journal article Open Access

Stationary Distributions of Continuous-Time Markov Chains: A Review of Theory and Truncation-Based Approximations

Kuntz, Juan; Thomas, Philipp; Stan, Guy-Bart; Barahona, Mauricio


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    <subfield code="u">Co-corresponding author. Department of Bioengineering, Imperial College London, London SW7 2AZ, UK</subfield>
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    <subfield code="u">Department of Mathematics and Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.</subfield>
    <subfield code="a">Kuntz, Juan</subfield>
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    <subfield code="a">Stationary Distributions of Continuous-Time Markov Chains: A Review of Theory and Truncation-Based Approximations</subfield>
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    <subfield code="a">&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Computing the stationary distributions of a continuous-time Markov chain (CTMC) involves solving a set of linear equations. In most cases of interest, the number of equations is infinite or too large, and the equations cannot be solved analytically or numerically. Several approximation schemes overcome this issue by truncating the state space to a manageable size. In this review, we first give a comprehensive theoretical account of the stationary distributions and their relation to the long-term behaviour of CTMCs that is readily accessible to non-experts and free of irreducibility assumptions made in standard texts. We then review truncation-based approximation schemes for CTMCs with infinite state spaces paying particular attention to the schemes&amp;#39; convergence and the errors they introduce, and we illustrate their performance with an example of a stochastic reaction network of relevance in biology and chemistry. We conclude by elaborating on computational trade-offs associated with error control and several open questions.&lt;/p&gt;

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