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A Monte Carlo Method For Calculating Bayesian Uncertainties In Internal Dosimetry

机译：内部剂量学中贝叶斯不确定性的蒙特卡洛方法

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摘要

This paper presents a novel Monte Carlo method (WeLMoS, Weighted Likelihood Monte-Carlo sampling method) that has been developed to perform Bayesian analyses of monitoring data. The WeLMoS method randomly samples parameters from continuous prior probability distributions and then weights each vector by its likelihood (i.e. its goodness of fit to the measurement data). Furthermore, in order to quality assure the method, and assess its strengths and weaknesses, a second method (MCMC, Markov chain Monte Carlo) has also been developed. The MCMC method uses the Metropolis algorithm to sample directly from the posterior distribution of parameters. The methods are evaluated and compared using an artificially generated case involving an exposure to a plutonium nitrate aerosol. In addition to calculating the uncertainty on internal dose, the methods can also calculate the probability distribution of model parameter values given the observed data. In other words, the techniques provide a powerful tool to obtain the estimates of parameter values that best fit the data and the associated uncertainty on these estimates. Current applications of the methodology, including the determination of lung solubility parameters, from volunteer and cohort data, are also discussed.
机译：本文介绍了一种新颖的蒙特卡洛方法（WeLMoS，加权似然蒙特卡洛采样方法），该方法已开发用于执行监视数据的贝叶斯分析。 WeLMoS方法从连续的先验概率分布中随机采样参数，然后根据其似然性（即其与测量数据的拟合优度）对每个向量加权。此外，为了质量保证该方法并评估其优缺点，还开发了第二种方法（MCMC，马尔可夫链蒙特卡洛）。 MCMC方法使用Metropolis算法直接从参数的后验分布进行采样。使用涉及硝酸nitrate气溶胶的人工产生的案例对方法进行评估和比较。除了计算内部剂量的不确定性之外，这些方法还可以根据给定的观察数据来计算模型参数值的概率分布。换句话说，这些技术提供了一个强大的工具，可以获取最适合数据的参数值的估计以及这些估计的相关不确定性。还讨论了该方法的当前应用，包括根据志愿者和队列数据确定肺溶解度参数。

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