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A Statistical Approach to the Development of Progress Plans Utilizing Bayesian Methods and Expert Judgment

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TLDR
In this paper, a methodology for progress plan development is proposed, which involves the elicitation of expert judgments to formulate probability distributions that reflect the expected values/estimates used to establish progress plans.
Abstract
: The development of progress plans for each identified technical performance parameter (TPP) is a critical element of technical performance measurement. The measured values of TPPs are referred to as technical performance measures (TPMs). These terms are used interchangeably; however, TPMs more directly reflect how technical progress and technical risk are measured and evaluated. Progress plans, or planned performance profiles, are crucial to effective risk assessment; however, methods for developing these plans are subjective in nature, have no statistical basis or criteria as a rule, and are not sufficiently addressed in literature. The methodology proposed herein for progress plan development will involve the elicitation of expert judgments to formulate probability distributions that reflect the expected values/estimates used to establish progress plans.

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Citations
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Journal ArticleDOI

Improving expert forecasts in reliability: Application and evidence for structured elicitation protocols

TL;DR: Structured elicitation protocols have been advocated to improve expert judgements, yet their application in reliability is challenged by a lack of examples or evidence that they improve judgements.
Journal ArticleDOI

The Development of Progress Plans Using a Performance-Based Expert Judgment Model to Assess Technical Performance and Risk

TL;DR: The Expert-weighted Technical Risk Index methodology proposed in this article introduces a well-established method for mathematically combining expert judgment into the realm of systems engineering to develop predictive progress plans for technical performance estimation and risk analysis.

Usng subjective percentiles and test data for estimating fragility functions

L.L. George, +1 more
TL;DR: In this paper, a composite fragility function for combining several failure modes was proposed, where subjective percentiles were treated as independent estimates of percentiles and test data was used to estimate subjective fragility functions.
Journal ArticleDOI

A Performance‐based Statistical Expert Judgment Model to Assess Technical Performance and Risk

TL;DR: In this article, a performance-based method of mathematically combining quantified expert opinion for technical performance estimation and risk analysis is proposed, based on the technical risk index distribution method developed by Lewis, Mazzuchi and Sarkani.
References
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Journal ArticleDOI

Combining Probability Distributions from Dependent Information Sources

TL;DR: In the face of uncertainty, all available information should be used to make inferences or decisions as mentioned in this paper, when probability distributions for an uncertain quantity are obtained from experts, models, or models.
Journal ArticleDOI

The Assessment of Probability Distributions from Expert Opinions with an Application to Seismic Fragility Curves

TL;DR: In this paper, a method for estimating a probability distribution using estimates of its percentiles provided by experts is developed for estimating the conditional probability of equipment failure given a seismically induced stress.
Journal ArticleDOI

Probability Judgments for Continuous Quantities: Linear Combinations and Calibration

Stephen C. Hora
- 01 May 2004 - 
TL;DR: A method of measuring calibration for continuous probability distributions is presented and it is demonstrated, both by example and empirically, that an equally weighted linear combination of experts who tend to be "overconfident" can produce distributions that are better calibrated than the experts' individual distributions.
ReportDOI

Technical Measurement. A Collaborative Project of PSM, INCOSE, and Industry

TL;DR: In this paper, the authors provide information on implementing technical measurement on a project, including measures of effectiveness (MOEs), key performance parameters (KPPs), measures of performance (MOPs), and technical performance measures (TPMs).