Low variance couplings for stochastic models of intracellular processes with time-dependent rate functions
DF Anderson, C Yuan - Bulletin of mathematical biology, 2019 - Springer
Bulletin of mathematical biology, 2019•Springer
A number of coupling strategies are presented for stochastically modeled biochemical
processes with time-dependent parameters. In particular, the stacked coupling is introduced
and is shown via a number of examples to provide an exceptionally low variance between
the generated paths. This coupling will be useful in the numerical computation of parametric
sensitivities and the fast estimation of expectations via multilevel Monte Carlo methods. We
provide the requisite estimators in both cases.
processes with time-dependent parameters. In particular, the stacked coupling is introduced
and is shown via a number of examples to provide an exceptionally low variance between
the generated paths. This coupling will be useful in the numerical computation of parametric
sensitivities and the fast estimation of expectations via multilevel Monte Carlo methods. We
provide the requisite estimators in both cases.
Abstract
A number of coupling strategies are presented for stochastically modeled biochemical processes with time-dependent parameters. In particular, the stacked coupling is introduced and is shown via a number of examples to provide an exceptionally low variance between the generated paths. This coupling will be useful in the numerical computation of parametric sensitivities and the fast estimation of expectations via multilevel Monte Carlo methods. We provide the requisite estimators in both cases.
Springer
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