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arXiv · 1706.00348

Efficient Low-Order Approximation of First-Passage Time Distributions

David Schnoerr·Botond Cseke·Ramon Grima·Guido Sanguinetti
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Abstract

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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David Schnoerr, Botond Cseke, Ramon Grima, Guido Sanguinetti. 2017-06-01. Efficient Low-Order Approximation of First-Passage Time Distributions. https://doi.org/10.1103/physrevlett.119.210601

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