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- W3098898981 abstract "Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks. We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies. The generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included." @default.
- W3098898981 created "2020-11-23" @default.
- W3098898981 creator A5012780830 @default.
- W3098898981 creator A5089228948 @default.
- W3098898981 date "2016-10-21" @default.
- W3098898981 modified "2023-10-17" @default.
- W3098898981 title "Generalized method of moments for estimating parameters of stochastic reaction networks" @default.
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- W3098898981 doi "https://doi.org/10.1186/s12918-016-0342-8" @default.
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