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Efficient low-order approximation of first-passage time distributions

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Original languageEnglish
Article number210601
Number of pages5
JournalPhysical Review Letters
Publication statusPublished - 20 Nov 2017


We consider the problem of computing first-passage time distributions for reaction processes modelled by master equations. We show that this generally intractable class of problems is equivalent to a sequential Bayesian inference problem for an auxiliary observation process. The solution can be approximated efficiently by solving a closed set of coupled ordinary differential equations (for the low-order moments of the process) whose size scales with the number of species. We apply it to an epidemic model and a trimerisation process, and show good agreement with stochastic simulations.

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