Jane M. Booker

Eliciting and analyzing expert judgment: A practical guide

Eliciting and analyzing expert judgment , Eliciting and analyzing expert judgment , کتابخانه دیجیتال جندی شاپور اهوازPreface to ASA-SIAM Edition Preface List of Figures List of Tables List of Examples Part I. Introduction to Expert Judgment. 1. Introduction 2. Common Questions and Pitfalls Concerning Expert Judgment 3. Background on Human Problem Solving and Bias Part II. Elicitation Procedures. 4. Selecting the Question Areas and Questions 5. Refining the Questions 6. Selecting and Motivating the Experts 7. Selecting the Components of Elicitation 8. Designing and Tailoring the Elicitation 9. Practicing the Elicitation and Training the Project Personnel 10. Conducting the Elicitation Part III. Analysis Procedures. 11. Introducing the Techniques for Analysis of Expert Judgment Data 12. Initial Look at the Data_The First Analyses 13. Understanding the Data Base Structure 14. Correlation and Bias Detection 15. Model Formation 16. Combining Responses_Aggregation 17. Characterizing Uncertainties 18. Making Inferences Appendix A. SAATY Appendix B. MCBETA Appendix C. EMPIRICAL Appendix D. BOOT Glossary of Expert Judgment Terms References Index.

Membership Functions and Probability Measures of Fuzzy Sets

The notion of fuzzy sets has proven useful in the context of control theory, pattern recognition, and medical diagnosis. However, it has also spawned the view that classical probability theory is unable to deal with uncertainties in natural language and machine learning, so that alternatives to probability are needed. One such alternative is what is known as “possibility theory.” Such alternatives have come into being because past attempts at making fuzzy set theory and probability theory work in concert have been unsuccessful. The purpose of this article is to develop a line of argument that demonstrates that probability theory has a sufficiently rich structure for incorporating fuzzy sets within its framework. Thus probabilities of fuzzy events can be logically induced. The philosophical underpinnings that make this happen are a subjectivistic interpretation of probability, an introduction of Laplaces famous genie, and the mathematics of encoding expert testimony. The benefit of making probability theory work in concert with fuzzy set theory is an ability to deal with different kinds of uncertainties that may arise within the same problem.

Fuzzy logic and probability applications: bridging the gap

Preface Section I. Fundamentals 1. Introduction 2. Fuzzy Set Theory, Fuzzy Logic, and Fuzzy Systems 3. Probability Theory 4. Bayesian Methods 5. Considerations for Using Fuzzy Set Theory and Probability Theory 6. Guidelines for Eliciting Expert Judgment as Probabilities or Fuzzy Logic Section II. Applications 7. Image Enhancement. Probability Versus Fuzzy Expert Systems 8. Engineering Process Control 9. Structural Safety Analysis. A Combined Fuzzy and Probability Approach 10. Aircraft Integrity and Reliability 11. Auto Reliability Project 12. Control Charts for Statistical Process Control 13. Fault Tree Logic Models 14. Uncertainty Distributions Using Fuzzy Logic 15. Signal Validation Using Bayesian Belief Networks and Fuzzy Logic.

Decision analysis in project management: An overview

The authors include very general topical summaries in utility theory, mathematical programming, statistical methods, scoring and ranking methods, and cognitive science. The emphasis is on the role of decision analysis in the management of research and development projects. The intent is to provide nonspecialists with a broad survey of the many facets of decision theory.

Testing the untestable: reliability in the 21st century

As science and technology become increasingly sophisticated, government and industry are relying more and more on sciences advanced methods to determine reliability. Unfortunately, political, economic, time, and other constraints imposed by the real world, inhibit the ability of researchers to calculate reliability efficiently and accurately. Because of such constraints, reliability must undergo an evolutionary change. The first step in this evolution is to re-interpret the concept so that it meets the new centurys needs. The next step is to quantify reliability using both empirical methods and auxiliary data sources, such as expert knowledge, corporate memory, and mathematical modeling and simulation.

Ch. 11. Predict: A new approach to product development and lifetime assessment using information integration technology

Publisher Summary Performance and reliability evaluation with diverse information combination and tracking (PREDICT) is a successful example of information integration technology that has been applied in two parallel applications: automotive system development and stockpile physics packages in nuclear weapons. This chapter outlines the applications, implementation steps, expert judgment, statistical tools, and decision making that make up the PREDICT methodology. PREDICT has demonstrated its effectiveness for expertise capture, reliability, and performance estimation in the nuclear weapons program and for concept system development in the automotive industry. In the post–Cold War era, the basic philosophy of information integration has been positively affecting the certification process of the nuclear systems. This same philosophy has been providing the formal structure for taking advantage of a companys greatest asset—the knowledge and expertise of its engineers and designers.

A Bayes sensitivity analysis when using the beta distribution as a prior

Results that illustrate the determination of the parameters of a beta prior distribution when either the mean and one percentile or two percentiles are given are presented. Further results show how sensitive the parameters of the beta prior are to errors in the given values. The sensitivity of the probability distribution of the reliability of a system to errors in either the given mean and percentile or the given percentiles of the component beta priors is studied. Included in the study are several systems, ranging from simple ones that occur as subsystems of real systems up to a real type of system consisting of 74 components. >

Characterizing reliability in a product/process design-assurance program

Just as estimates of cost and program timing are critical factors to be known and monitored during a new product development program, so too is the reliability perspective. This paper describes an approach to reliability modeling that encompasses the impact of both product and manufacturing process design on the distribution (characterizing the uncertainty) of reliability over time. It further describes the elicitation of expert judgment which is used to quantify the initial reliability estimate, including uncertainty. Finally, it describes a Bayesian updating approach which is applicable throughout the development program, and which accommodates a wide variety of possible new information. Although the model is rigorous in its execution; a user-friendly approximation is also described which may be useful to the product development team for purposes of test and validation planning.

PREDICT: a case study, using fuzzy logic

Los Alamos National Laboratory, Design For Reliability, Inc., and others, have worked together to develop PREDICT, a new methodology to characterize the reliability of a new product during its development program. Rather than conducting testing after hardware has been built, and developing statistical confidence bands around the results, this updating approach starts with an early reliability estimate characterized by large uncertainty, and then proceeds to reduce the uncertainty by folding in fresh information in a Bayesian framework. A considerable amount of knowledge is available at the beginning of a program in the form of expert judgment that helps to provide the initial estimate. This estimate is then continually updated as substantial and varied information becomes available during the course of the development program. This paper presents a case study of the application of PREDICT, including an example of the use of fuzzy logic, with the objective of further describing the methodology.

Estimating a Proportion Using Stratified Data From Both Convenience and Random Samples

Estimating the proportion of an attribute present in a population can be challenging when the population is stratified by lots produced by a common manufacturing process and the available data arise from both random and convenience samples. Moreover, all of the lots may not have been sampled. This article proposes a Bayesian methodology for making inferences about a proportion that properly accounts for the potential bias of the convenience samples, the stratification by lots, and the fact that not all of the lots have been sampled. The methodology is illustrated with a simulated population; however, the solution is motivated by a similar, but proprietary, production situation.