Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics

Arampatzis, Georgios; Katsoulakis, Markos A.; Rey-Bellet, Luc

doi:10.1063/1.4943388

Title: Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics

Journal Article · Fri Mar 11 00:00:00 EST 2016 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.4943388· OSTI ID:1470323

Arampatzis, Georgios ^[1]; Katsoulakis, Markos A. ^[2];

^[2]

Univ. of Massachusetts, Amherst, MA (United States); ETH Zurich, Zurich (Switzerland)
Univ. of Massachusetts, Amherst, MA (United States)

Here, we demonstrate that centered likelihood ratio estimators for the sensitivity indices of complex stochastic dynamics are highly efficient with low, constant in time variance and consequently they are suitable for sensitivity analysis in long-time and steady-state regimes. These estimators rely on a new covariance formulation of the likelihood ratio that includes as a submatrix a Fisher information matrix for stochastic dynamics and can also be used for fast screening of insensitive parameters and parameter combinations. The proposed methods are applicable to broad classes of stochastic dynamics such as chemical reaction networks, Langevin-type equations and stochastic models in finance, including systems with a high dimensional parameter space and/or disparate decorrelation times between different observables. Furthermore, they are simple to implement as a standard observable in any existing simulation algorithm without additional modifications.

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Research Organization:: Univ. of Massachusetts, Amherst, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: SC0010723

OSTI ID:: 1470323

Alternate ID(s):: OSTI ID: 1241424

Journal Information:: Journal of Chemical Physics, Vol. 144, Issue 10; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 9 works

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Cited By (4)

Girsanov reweighting for path ensembles and Markov state models Donati, L.; Hartmann, C.; Keller, B. G. The Journal of Chemical Physics, Vol. 146, Issue 24 https://doi.org/10.1063/1.4989474	journal	June 2017
Acceleration and sensitivity analysis of lattice kinetic Monte Carlo simulations using parallel processing and rate constant rescaling Núñez, M.; Robie, T.; Vlachos, D. G. The Journal of Chemical Physics, Vol. 147, Issue 16 https://doi.org/10.1063/1.4998926	journal	October 2017
Estimation of the infinitesimal generator by square-root approximation Donati, Luca; Heida, Martin; Keller, Bettina G. Journal of Physics: Condensed Matter, Vol. 30, Issue 42 https://doi.org/10.1088/1361-648x/aadfc8	journal	October 2018
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