Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the transition probabilities
Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the transition probabilities
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DOI：
10.1049/iet-syb.2014.0020
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发表时间：
2015-10-01
影响因子：
2.3
通讯作者：
Sahle, Sven
中科院分区：
文献类型：
--
作者：
Zimmer, Christoph;Sahle, Sven
从实验数据估计模型参数是系统生物学中处理计算模型的一项关键技术。随着随机模型的日益重要，随机模型的参数估计方法也越来越受到人们的关注。本文对随机系统多重打靶法的参数估计方法进行了推广。似然函数的转移概率用正态分布近似。在连续测量之间的间隔上，使用线性噪声近似来计算平均值和方差。系统仅以比总观测视界短的间隔进行近似，这一事实允许处理内在随机性的影响。这项研究提出了延期对成功估计参数至关重要的情景，以及延期益处不大的情景。此外，它还将估计结果与可逆跳跃技术进行了比较，表明该近似不会导致精度损失。由于该方法不是基于随机模拟或分布的近似抽样，其计算速度与传统的最小二乘参数估计方法相当。
Estimating model parameters from experimental data is a crucial technique for working with computational models in systems biology. Since stochastic models are increasingly important, parameter estimation methods for stochastic modelling are also of increasing interest. This study presents an extension to the multiple shooting for stochastic systems (MSS)' method for parameter estimation. The transition probabilities of the likelihood function are approximated with normal distributions. Means and variances are calculated with a linear noise approximation on the interval between succeeding measurements. The fact that the system is only approximated on intervals which are short in comparison with the total observation horizon allows to deal with effects of the intrinsic stochasticity. The study presents scenarios in which the extension is essential for successfully estimating the parameters and scenarios in which the extension is of modest benefit. Furthermore, it compares the estimation results with reversible jump techniques showing that the approximation does not lead to a loss of accuracy. Since the method is not based on stochastic simulations or approximative sampling of distributions, its computational speed is comparable with conventional least-squares parameter estimation methods.