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JJF 1059.2-2012

JJF 1059.2-2012_English: PDF (JJF1059.2-2012)
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JJF 1059.2-2012English1039 Add to Cart Days<=7 Monte Carlo Method for Evaluation of Measurement Uncertainty JJF 1059.2-2012 Valid JJF 1059.2-2012
 

BASIC DATA
Standard ID JJF 1059.2-2012 (JJF1059.2-2012)
Description (Translated English) Monte Carlo Method for Evaluation of Measurement Uncertainty
Sector / Industry Metrology & Measurement Industry Standard
Word Count Estimation 45,480
Quoted Standard JJF 1059.1-2012; GB/T 3358.1-2009; GB/T 8170-2008; ISO/IEC GUIDE 98-3-2008; ISO/IEC GUIDE 98-3/SUP 1-2008; ISO 3534-1-2006; ISO/IEC GUIDE 99-2007
Drafting Organization Beijing Institute of Technology
Administrative Organization National Technical Committee of Legal Metrology management
Regulation (derived from) AQSIQ Announcement No. 212 of 2012
Issuing agency(ies) State Administration of Quality Supervision, Inspection and Quarantine
Summary This standard applies to an arbitrary number by the probability density function (PDF) characterization of the amount of input and a single output models. The measurement uncertainty specification provides a common numerical methods. This specification is

JJF 1059.2-2012
Monte Carlo Method for Evaluation of Measurement Uncertainty
People's Republic of China National Metrology Technical Specifications
Monte Carlo method for evaluation of measurement uncertainty
Issued on. 2012-12-21
2013-06-21 implementation
The State Administration of Quality Supervision, Inspection and Quarantine released
Monte Carlo method for evaluation of measurement uncertainty
Focal point. the National Technical Committee of Legal Metrology measurement management
Drafted by. Beijing Institute of Technology
China Institute of Metrology
AQSIQ Measurement Department
Jiangsu Province Institute of Metrology
This specification commissioned the National Technical Committee on Metering Management is responsible for interpretation of legal metrology
Drafters of this specification.
Zhou Tao Geng Beijing Institute of Technology
Yede Pei
Sha given country Beijing Institute of Technology
Compliance original East China Institute of Metrology
Shichang Yan
Chen AQSIQ Measurement Department
Zhao Feng, Jiangsu Province Institute of Metrology
table of Contents
Introduction (Ⅱ)
1 Scope (1)
2. References (1)
3 Terms and definitions (2)
4 Monte Carlo method (5)
4.1 Monte Carlo method of step (5)
4.2 to establish the model (6)
4.3 Monte Carlo trials (6)
4.4 input sample value and the probability distribution model calculation (6)
4.5 Discrete output distribution function representation (7)
4.6 output and standard uncertainty (7)
The output of the interval containing 4.7 (7)
4.8 Adaptive Monte Carlo method (8)
5 report the results (9)
6 Monte Carlo Method validation results GUM method (10)
6.1 Monte Carlo Method validation results GUM method (10)
6.2 Monte Carlo is used to verify the number of trials (11) GUM law
Appendix A Common input probability density function (12)
Appendix B Examples of distribution by Monte Carlo method propagation probability (16)
Comparison C MCM and the GUM method appendix (31)
Appendix D distribution of the basic principle of propagation (32)
Appendix E basic symbols (33)
Appendix F Glossary of English-Chinese (36)
introduction
This specification defines the Monte Carlo method (MonteCarloMethod, referred MCM) evaluation and expression of measurement
Uncertainty methods, its core is the use of MCM in the establishment of the measurement model probability distributions Biography
broadcast. This instruction applies to having a plurality of inputs and a single output of the measurement model.
This specification is JJF 1059.1-2012 "Evaluation and Expression of Uncertainty in Measurement," the supplement, equivalent to the use of international
Standard ISO /IEC GUIDE98-3.2008 (GUM) Annex 1. "Monte Carlo method propagation probability distribution"
(Supplement1. PropagationofdistributionsusingaMonteCarlomethod), but in the structure of series
Rafts made major changes.
MCM described in this specification is particularly suitable for the following three conditions.
--- Obviously nonlinear measurement model;
--- Input probability density function (probabilitydensityfunction, abbreviated PDF) obviously asymmetrical;
--- Output of PDF obvious deviation from normal or t-distribution, in particular, distribution is significantly asymmetric
occasion.
In the above case, according to its standard output estimate uncertainty identified may become JJF 1059.1
Reliable, or is likely to cause or to estimate the expanded uncertainty interval contains unrealistic.
In JJF 1059.1, enter the amount of information is the best estimate of the standard uncertainty, freedom and covariance.
In the present specification, the input information is input in PDF.
In JJF 1059.1, the combined standard uncertainty is determined by the amount of output uncertainty propagation law. In the present
Specification, using MCM propagation probability distribution to determine an estimated value measured and contain intervals.
Appendix A of this specification "Common input probability density function," Appendix B "using the Monte Carlo method propagation probability
Examples of distrib