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首页> 外文学位 >Quantitative approach to technical performance measurement and technical risk analysis utilizing Bayesian methods and Monte Carlo simulation.
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Quantitative approach to technical performance measurement and technical risk analysis utilizing Bayesian methods and Monte Carlo simulation.

机译：利用贝叶斯方法和蒙特卡洛模拟对技术性能进行度量和技术风险分析的定量方法。

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摘要

Risk on a project or program is typically evaluated in terms of the triple constraint: scope, time, and cost. The Monte Carlo method (Metropolis & Ulam, 1949) is a widely accepted risk analysis technique and is deemed to be an effective way of analyzing the uncertainty associated with cost and schedule risks. More systems engineers and risk managers are taking advantage of this risk analysis technique to perform independent risk assessments on either cost or schedule however, the analysis of technical risk using a statistical approach has not been formulated to a significant degree in the literature.This research study will explore the use of a statistical versus a deterministic approach to independently assess the risks associated with technical performance. The methodology discussed transforms a deterministic risk assessment model into a statistical model. Expert judgment is used within a Bayesian framework to develop baseline expected values for technical performance. Technical Performance Measurement (TPM), Monte Carlo simulation, and Risk software are used to calculate a system level Technical Risk Index Distribution (TRID). The TRID framework defines distribution ranges for each TPM and formulates risk index distributions. Actual data from a DoD TPM implementation project is used to validate the TRID framework. The proposed statistical model is verified on a hypothetical system where quantifiable TPMs have been identified that meet the criterion in the model proposed.Keywords. Monte Carlo simulation, TPMs, technical performance measures, technical risk, technical performance risk, Monte Carlo risk analysis, sensitivity analysis, technical risk analysis, program management, risk management, project management, risk analysis, risk, quantitative risk analysis, expert judgment, Bayes Theorem, Bayesian methods, expert opinion.
机译：通常根据三重约束条件评估项目或程序的风险：范围，时间和成本。蒙特卡罗方法（Metropolis＆Ulam，1949）是一种被广泛接受的风险分析技术，被认为是分析与成本和进度风险相关的不确定性的有效方法。越来越多的系统工程师和风险管理人员正在利用这种风险分析技术对成本或进度进行独立的风险评估，但是，在文献中并没有在很大程度上制定使用统计方法对技术风险进行分析的方法。将探索使用统计方法还是确定性方法来独立评估与技术性能相关的风险。讨论的方法将确定性风险评估模型转换为统计模型。在贝叶斯框架内使用专家判断来制定技术性能的基线期望值。技术性能度量（TPM），蒙特卡洛模拟和Risk软件用于计算系统级技术风险指数分布（TRID）。 TRID框架为每个TPM定义了分布范围，并制定了风险指数分布。来自国防部TPM实施项目的实际数据用于验证TRID框架。在假设的系统上验证了提出的统计模型，在该系统上已确定了可量化的TPM，这些TPM符合提出的模型中的标准。蒙特卡罗模拟，TPM，技术性能指标，技术风险，技术性能风险，蒙特卡洛风险分析，敏感性分析，技术风险分析，程序管理，风险管理，项目管理，风险分析，风险，定量风险分析，专家判断，贝叶斯定理，贝叶斯方法，专家意见。

著录项

  • 作者

    Lewis, Tiffany Lorraine.;

  • 作者单位

    The George Washington University.;

  • 授予单位 The George Washington University.;
  • 学科 Statistics.Operations Research.Engineering System Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 290 p.
  • 总页数 290
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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