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Development of empirical possibility distributions in risk analysis.

机译：风险分析中经验可能性分布的发展。

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摘要

The goal of this dissertation is to develop methods that can be used to systematically, consistently and logically generate uncertainty distributions that do not assume randomness in the system. Uncertainty is pervasive in all systems; even more so in systems such as risk assessment where information is accumulated from varied sources that are inherently complex and where the parameters of the system cannot be assumed to be random due to data insufficiencies. On the other hand, though non-probabilistic methods have been shown to be excellent tools to capture non-random uncertainty their application has been limited by their inability to offer methods for deriving distributions from empirical data. The motivation behind this dissertation is to find new models to overcome the difficulties inherent in the ability of probability theory to model non-random uncertainty, and to adapt these new models to capture both random and non-random uncertainty as empirical possibility distributions. Two novel methods based on possibility theory are proposed to represent uncertainty in systems.; The property of consonance in data, where evidence leads an expert/model to inductively make judgments that converge to one possible outcome, is assumed in the development contained herein. Crisp notions of classical logic with a truth-value of either 0 or 1 (assuming complete evidence) on disjoint sets/outcomes (Ai ∩ Aj = Ø) are replaced by softer notions where truth-values that range between 0 and 1 are defined over overlapping sets/outcomes (Ai ∩ Ai ≠ Ø). The new methods exploit set-based mechanisms such as interval analysis and cluster analysis to accomplish the task of deriving possibility distributions and to quantitatively represent imprecise knowledge. Relaxation of the axiom of additivity with the proper assumption of sub-additivity, allows the developments made here to represent inexact, imprecise, incomplete, and incoherent information in a more realistic and consistent manner. Two novel approaches for deriving possibility distributions are developed in this dissertation: (i) Method I is used for non-consistent and non-disjoint data intervals; and (ii) Method II is used for point estimates and disjoint data intervals. The new methods are illustrated through two case studies in human health risk assessment of radon gas exposure.
机译：本文的目的是开发可用于系统，一致和逻辑地生成不确定性分布的方法，这些不确定性分布在系统中不具有随机性。不确定性在所有系统中普遍存在。在诸如风险评估之类的系统中，甚至更是如此，在这种系统中，信息是从本质上很复杂的各种来源收集的，并且由于数据不足而不能假定系统的参数是随机的。另一方面，尽管已证明非概率方法是捕获非随机不确定性的出色工具，但它们的应用受到限制，因为它们无法提供从经验数据中得出分布的方法。本文的目的是要找到新的模型来克服概率论模型对非随机不确定性建模的能力所固有的困难，并使这些新模型适应于将随机和非随机不确定性都作为经验可能性分布来捕获。提出了两种基于可能性理论的新颖方法来表示系统中的不确定性。本文包含的发展假设了数据中的共鸣属性，即证据导致专家/模型感应地做出收敛到一个可能结果的判断。关于不相交集/结果（ A i ∩ A ）的真值（真值）为0或1（假定完整的证据）的经典逻辑的酥脆概念j =Ø）被更柔和的概念代替，其中在重叠的集/结果（ A i ∩ A i ≠Ø）。新方法利用诸如间隔分析和聚类分析的基于集合的机制来完成推导可能性分布的任务并定量表示不精确的知识。通过适当地假设次可加性来放松可加性公理，可以使此处所做的发展以更现实和一致的方式表示不精确，不精确，不完整和不连贯的信息。本文提出了两种推导可能性分布的新颖方法：（i）方法I用于不一致和不相交的数据间隔; （ii）方法II用于点估计和不相交的数据间隔。通过在case气暴露于人类健康风险评估中的两个案例研究说明了新方法。

著录项

  • 作者

    Donald, Sunil.;

  • 作者单位

    The University of New Mexico.;

  • 授予单位 The University of New Mexico.;
  • 学科 Engineering Environmental.; Mathematics.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 p.2854
  • 总页数 187
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 环境污染及其防治;
  • 关键词

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