NASA SP-2009-569-2009 Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis《NASA概率性风险和可靠性分析的贝叶斯推论》.pdf
《NASA SP-2009-569-2009 Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis《NASA概率性风险和可靠性分析的贝叶斯推论》.pdf》由会员分享,可在线阅读,更多相关《NASA SP-2009-569-2009 Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis《NASA概率性风险和可靠性分析的贝叶斯推论》.pdf(275页珍藏版)》请在麦多课文档分享上搜索。
1、 Provided by IHSNot for ResaleNo reproduction or networking permitted without license from IHS-,-,-NASA/SP-2009-569 Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis Dr. Homayoon Dezfuli NASA Project Manager, NASA Headquarters, Washington, DC Dana Kelly Idaho National Laborator
2、y, Idaho Falls, ID Dr. Curtis Smith Idaho National Laboratory, Idaho Falls, ID Kurt Vedros Idaho National Laboratory, Idaho Falls, ID William Galyean Idaho National Laboratory, Idaho Falls, ID National Aeronautics and Space Administration June 2009Provided by IHSNot for ResaleNo reproduction or netw
3、orking permitted without license from IHS-,-,-NASA STI Program . in Profile Since its founding, NASA has been dedicated to the advancement of aeronautics and space science. The NASA scientific and technical information (STI) program plays a key part in helping NASA maintain this important role. The
4、NASA STI program operates under the auspices of the Agency Chief Information Officer. It collects, organizes, provides for archiving, and disseminates NASAs STI. The NASA STI program provides access to the NASA Aeronautics and Space Database and its public interface, the NASA Technical Report Server
5、, thus providing one of the largest collections of aeronautical and space science STI in the world. Results are published in both non-NASA channels and by NASA in the NASA STI Report Series, which includes the following report types: TECHNICAL PUBLICATION. Reports of completed research or a major si
6、gnificant phase of research that present the results of NASA Programs and include extensive data or theoretical analysis. Includes compilations of significant scientific and technical data and information deemed to be of continuing reference value. NASA counterpart of peer-reviewed formal profession
7、al papers but has less stringent limitations on manuscript length and extent of graphic presentations. TECHNICAL MEMORANDUM. Scientific and technical findings that are preliminary or of specialized interest, e.g., quick release reports, working papers, and bibliographies that contain minimal annotat
8、ion. Does not contain extensive analysis. CONTRACTOR REPORT. Scientific and technical findings by NASA-sponsored contractors and grantees. CONFERENCE PUBLICATION. Collected papers from scientific and technical conferences, symposia, seminars, or other meetings sponsored or co-sponsored by NASA. SPEC
9、IAL PUBLICATION. Scientific, technical, or historical information from NASA programs, projects, and missions, often concerned with subjects having substantial public interest. TECHNICAL TRANSLATION. English-language translations of foreign scientific and technical material pertinent to NASAs mission
10、. Specialized services also include creating custom thesauri, building customized databases, and organizing and publishing research results. For more information about the NASA STI program, see the following: Access the NASA STI program home page at http:/www.sti.nasa.gov E-mail your question via th
11、e Internet to helpsti.nasa.gov Fax your question to the NASA STI Help Desk at 443-757-5803 Phone the NASA STI Help Desk at 443-757-5802 Write to: NASA STI Help Desk NASA Center for AeroSpace Information 7115 Standard Drive Hanover, MD 21076-1320 Provided by IHSNot for ResaleNo reproduction or networ
12、king permitted without license from IHS-,-,-Provided by IHSNot for ResaleNo reproduction or networking permitted without license from IHS-,-,-I Foreword This NASA-HANDBOOK is published by the National Aeronautics and Space Administration (NASA) to provide a Bayesian foundation for framing probabilis
13、tic problems and performing inference on these problems. It is aimed at scientists and engineers and provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models. The
14、overall approach taken in this document is to give both a broad perspective on data analysis issues and a narrow focus on the methods required to implement a comprehensive database repository. It is intended for use across NASA. Recent years have seen significant advances in the use of risk analysis
15、 at NASA. These advances are reflected both in the state of practice of risk analysis within projects, and in the status of several NASA requirements and procedural documents. Because risk and reliability models are intended to support decision processes, it is critical that inference methods used i
16、n these models be robust and technically sound. To this end, the Office of Safety and Mission Assurance (OSMA) undertook the development of this document. This activity, along with other ongoing OSMA-sponsored projects related to risk and reliability, supports the attainment of the holistic and risk
17、-informed decision-making environment that NASA intends to adopt. This document is not intended to prescribe any technical procedure and/or software tool. The coverage of the technical topics is also limited with respect to (1) the historical genesis of Bayesian methods; (2) comparisons of “classica
18、l statistics” approaches with Bayesian ones; (3) the detailed mathematics of a particular method (unless needed to apply the method); and (5) a source of actual reliability or risk data/information. Additionally, this document is focused on hardware failures; excluded from the current scope are spec
19、ific inference approaches for phenomenological, software, and human failures. As with many disciplines, there are bound to be differences in technical and implementation approaches. The authors acknowledge that these differences exist and to the extent practical these instances have been identified.
20、 The Bayesian Inference handbook assumes that probabilistic inference problems range from simple, well-supported cases to complex, multi-dimensional problems. Consequently, the approaches provided to evaluate these diverse sets of issues range from single-line spreadsheet formula approaches to Monte
21、 Carlo-based sampling methods. As such, the level of analyst sophistication should be commensurate with the issue complexity and the selected computational engine. To assist analysts in applying the inference principles, the document provides “call out” boxes to provide definitions, warnings, and ti
22、ps. In addition, a hypothetical (but representative) system analysis and multiple examples are provided, as are methods to extend the analysis to accommodate real-world complications such as uncertain, censored, and disparate data. For most of the example problems, the Bayesian Inference handbook us
23、es a modern computational approach known as Markov chain Monte Carlo (MCMC). Salient references provide the technical basis and mechanics of MCMC approaches. MCMC methods work for simple cases, but more importantly, they work efficiently on very complex cases. Bayesian inference tends to become comp
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