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Bayesian parameter estimation for compartmental models in biology and physics.

机译:贝叶斯参数估计在生物学和物理学中的隔室模型。

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摘要

We demonstrated that the use of Bayes' Theorem, with the Box and Draper likelihood, to estimate the parameters of compartmental models is an improvement over current "ad hoc" parameter estimation methods. This method has several novel features that are useful in parameter estimation. First, the remaining parameter uncertainty is described by the posterior density, which cannot be obtained with classical regression methods. Second, highest posterior density contours can be used to illustrate uncertainty of parameter pairs, and their shape changes can be used to describe the influence of reduced data sets and/or different models on the estimation uncertainty. Third, information from other experiments and sources can be incorporated using appropriate prior distribution.;Two areas of application for this technique are biology and physics. Three biokinetic models were studied: the exchange of calcium at bone surfaces in beagles, human cerebral glucose metabolism, and the exchange of serum albumin in human. In physics, we estimated the half-lives of 226Ra, 222Rn and 218Po from simulated decay data. In addition, different reduced data sets were also examined for each model to show their influence on parameter uncertainty.;We applied the Bayesian method to two and three-compartment models. The presence of bimodality and "divergent" behavior in the posterior densities is new and unexpected. They are due to the likelihood and can be changed using prior information and/or the amount of data used. For example, the use of Normal priors stopped the "divergence" in the bone calcium study. However, it also introduced bimodality for all three parameters.;Interestingly, the omission of the extravascular space data in the serum albumin analysis did not diminish the estimation accuracy. Instead, estimation precision was increased for three of the four parameters, as indicated by smaller contours. When open contours are present, the estimation variances are large and can be infinite. Finally, we showed that radioactive decay can be described using compartmental models and that half-lives can be estimated using Box and Draper's method. As always, the most precise estimates were obtained using data for individual compartments.
机译:我们证明了使用带Box和Draper可能性的贝叶斯定理来估计车厢模型的参数是对当前“临时”参数估计方法的改进。该方法具有一些新颖的功能,可用于参数估计。首先,剩余参数不确定性由后验密度描述,而后验密度无法通过经典回归方法获得。其次,最高后验密度等高线可用于说明参数对的不确定性,其形状变化可用于描述简化的数据集和/或不同模型对估计不确定性的影响。第三,可以使用适当的先验分布来合并来自其他实验和来源的信息。;该技术的两个应用领域是生物学和物理学。研究了三种生物动力学模型:比格犬的骨表面钙交换,人脑葡萄糖代谢以及人血清白蛋白交换。在物理学中,我们从模拟衰变数据估算了226Ra,222Rn和218Po的半衰期。此外,还针对每个模型检查了不同的简化数据集,以显示它们对参数不确定性的影响。;我们将贝叶斯方法应用于两室和三室模型。后部密度中双峰和“发散”行为的出现是新的和出乎意料的。它们是由于可能性造成的,可以使用先验信息和/或使用的数据量进行更改。例如,使用“正常先验”可以阻止骨钙研究中的“分歧”。但是,它还为所有三个参数引入了双峰性。有趣的是,在血清白蛋白分析中遗漏了血管外空间数据并没有降低估计的准确性。取而代之的是,如较小的轮廓所示,对四个参数中的三个参数提高了估计精度。当存在开放轮廓时,估计方差很大并且可以无限大。最后,我们表明可以使用隔室模型描述放射性衰变,并且可以使用Box和Draper方法估算半衰期。与往常一样,使用各个舱室的数据可以获得最精确的估计。

著录项

  • 作者

    Lo, Yunnhon.;

  • 作者单位

    The University of Tennessee.;

  • 授予单位 The University of Tennessee.;
  • 学科 Biology Biostatistics.;Statistics.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 234 p.
  • 总页数 234
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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