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Parameter Estimation for Individuals-based Models of Biochemical Reactions

机译:基于个体的生物化学反应模型参数估计

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Parameter estimation is crucial for us to analyse the models, and such works of individuals-based models is still in the early stage of development. For the individuals-based models, there is no efficient methods to estimate the parameters due to the observed data with noise produced by inherent randomness of model. This paper, we utilize different methods that are well developed for parameter estimation of determined model which is constituted by ordinary differential equations (ODE) are also adapted to stochastic models. In this article, We use the population changes of aphids as a case study. We want to estimate the birth rate and the mortality of the aphids. An intuitive approach is least square method to estimate the parameters, and this application is very extensive. However, the problem of parameter identification is the most common issue of least square method in estimating parameters. In this article we show the latest progress in parameter estimation for individuals-based models of our study which bases on moment closure approximation technique. The combination of MCMC and likelihood function is a less used method in the estimation of stochastic model parameters. These two methods can overcome the problem of parameter identification in the least square.
机译:参数估计对我们分析模型至关重要,并且基于个人的模型的这些作品仍在开发的早期阶段。对于基于个人的模型,没有有效的方法来估计由于模型内固有的随机性产生的噪声而导致的参数。本文利用了对所确定模型的参数估计产生的不同方法,该模型由常微分方程(ode)构成也适用于随机模型。在本文中,我们使用蚜虫的人口变化作为案例研究。我们希望估计蚜虫的出生率和死亡率。直观的方法是估计参数的最小方形方法,此应用程序非常广泛。然而,参数识别问题是估计参数中最常见的最常见问题的问题。在本文中,我们显示了我们研究基于个人的参数估计的最新进展,这是矩阵近似技术的基础。 MCMC和似然函数的组合是在随机模型参数估计中的使用方法较少。这两种方法可以克服至少正方形中参数识别的问题。

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