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

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.
Authors:
 [1] ;  [2] ; ORCiD logo [2]
  1. Univ. of Massachusetts, Amherst, MA (United States); ETH Zurich, Zurich (Switzerland)
  2. Univ. of Massachusetts, Amherst, MA (United States)
Publication Date:
Grant/Contract Number:
SC0010723
Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 144; Journal Issue: 10; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Research Org:
Univ. of Massachusetts, Amherst, MA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
OSTI Identifier:
1470323
Alternate Identifier(s):
OSTI ID: 1241424