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Bayesian Estimation and MCMC Sampling for the Mortality Probability Model of Population

机译:人口死亡率概率模型的贝叶斯估计和MCMC抽样

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In this paper we consider the mortality probability model of population throughout the whole lifespan based on the mortality rate in the Weibull distribution, that is, the exponential constant of the mortality rate is transformed into a variable function. In the paper we provide the variable function with alternative methods. However the precision of linear function is less than that of nonlinear function with the same number of parameters. The paper proposes a nonlinear variable function as the exponential of mortality rate. Bayesian estimation provides a feasible treatment of the complicated model resorting to MCMC algorithms. Finally we carry out a research into the mortality probability model of Rattus norvegicus population. MC error precision of parameters reaches to 10-4. The results show us Bayesian estimation is an effective method using MCMC sampling the mortality probability model of population.
机译:在本文中,我们基于威布尔分布中的死亡率来考虑整个生命周期内人口的死亡率概率模型,即将死亡率的指数常数转换为变量函数。在本文中,我们为变量函数提供了替代方法。但是,在参数数量相同的情况下,线性函数的精度要小于非线性函数的精度。本文提出了一种非线性变量函数作为死亡率指数。贝叶斯估计为依靠MCMC算法的复杂模型提供了可行的解决方案。最后,我们对褐家鼠种群的死亡率概率模型进行了研究。参数的MC误差精度达到10-4。结果表明,贝叶斯估计是一种使用MCMC采样人口死亡率概率模型的有效方法。

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