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光释光(OSL)年代学模型是基于数理统计学的一类概率密度模型,它根据特定的假设条件对样品等效剂量(De)分布进行数学解释,由此估计具有不同沉积历史或者能够代表样品实际埋藏年龄的De组分。年龄模型参数估计常通过极大似然估计(MLE)算法实现,本文尝试了切片采样算法在年龄模型参数优化中的应用。切片采样属于一种马尔科夫链蒙特卡罗采样(MCMC)算法,能根据测量数据与模型的联合似然函数进行随机采样,由此获得参数的采样分布。本文编写了实现年龄模型切片采样算法的应用程序,并使用模拟及实测De数据验证了该算法估计的可靠性。相对于MLE算法,MCMC算法具有对参数初值依赖性低、误差估计更准确的特点,切片采样算法提供了实现释光年龄模型参数估计的一种新方法。
The OSL chronology model is a class of probability density models based on mathematical statistics that mathematically interprets the sample’s equivalent dose (De) distribution according to specific assumptions to estimate the distribution of samples with different deposition histories or to represent The sample actually buries the De component of age. The estimation of age model parameters is often implemented by the maximum likelihood estimation (MLE) algorithm. This paper attempts to apply the slice sampling algorithm to optimize the age model parameters. Slice sampling belongs to a kind of Markov chain Monte Carlo sampling (MCMC) algorithm, which can sample randomly according to the joint likelihood function of the measurement data and the model, thus obtaining the sample distribution of parameters. In this paper, an application to implement the age model slice sampling algorithm is compiled, and the reliability of the proposed algorithm is verified by using the simulated and measured data. Compared with the MLE algorithm, the MCMC algorithm has the characteristics of low dependence on the initial parameters and more accurate error estimation. The slice sampling algorithm provides a new method to estimate the parameters of the light-emitting age model.