Abstract
Parameter inference for stochastic kinetic models is a topic that spans many disciplines. Although it is possible to carry out exact inference using partial observations of a stochastic process, it is often computationally impractical. In this paper we use the moment closure approximation of the underlying stochastic process as a fast approximation of the likelihood. We show that this approximation is fast and accurate, even when the population numbers are small.
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Milner, P., Gillespie, C.S. & Wilkinson, D.J. Moment closure based parameter inference of stochastic kinetic models. Stat Comput 23, 287–295 (2013). https://doi.org/10.1007/s11222-011-9310-8
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DOI: https://doi.org/10.1007/s11222-011-9310-8