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机构地区:[1]中国科学院寒区旱区环境与工程研究所,兰州730000 [2]中国科学院青海盐湖研究所,西宁810008
出 处:《地理科学进展》2016年第1期78-88,共11页Progress in Geography
基 金:国家重点基础研究发展计划(973计划)项目(2013CB956000;2012CB426501)~~
摘 要: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。In Optically Stimulated Luminescence(OSL) dating, statistical age models used for equivalent dose(De) determination are probabilistic models constructed according to mathematical statistics. They are applied to distinguish De populations that are sedimentologically different or to determine a De value that represents the burial dose of a sample. Maximum likelihood estimation(MLE) method is routinely used to optimize parameters of an age model. In the present study, we used the Slice sampling algorithm to determine the parameters of age models. Slice sampling is a Markov chain Monte Carlo(MCMC) sampling method, which enables the sampling distributions of parameters to be obtained from the joint likelihood function that is determined by observations and the specified model. This study applied easily implemented and openly accessible numeric routines to performing the algorithm. We used artificial and measured datasets to check the reliability of the estimates.MCMC method is insensitive to the parameters' initial states, and the standard errors(or confidence intervals) of parameters assessed using this method are more reliable compared to those based on the Fisher information matrix constructed through numerical differentiation. Our results indicate that the Slice sampling method provides an alternative for age model optimization. Slice sampling method generates an informative estimation for the results of MLE method in age model application.
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