Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks

DOI10.1007/s11222-020-09965-3zbMath1471.60105arXiv1911.06286OpenAlexW3080678949MaRDI QIDQ2209728

Chiheb Ben Hammouda, Nadhir Ben Rached, Raúl Tempone

Publication date: 4 November 2020

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.06286

zbMATH Keywords

importance samplingcontinuous-time Markov chainsmultilevel Monte Carlostochastic reaction networksstochastic biological systems

Mathematics Subject Classification ID

Biochemistry, molecular biology (92C40) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Continuous-time Markov processes on discrete state spaces (60J27) Jump processes on discrete state spaces (60J74)

Related Items (4)

Probabilistic failure mechanisms via Monte Carlo simulations of complex microstructures ⋮ Multilevel Monte Carlo using approximate distributions of the CIR process ⋮ Learning-based importance sampling via stochastic optimal control for stochastic reaction networks ⋮ Multi-index ensemble Kalman filtering

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