Nonparametric Estimation of the Probability of Detection of Flaws in an Industrial Component, from Destructive and Nondestructive Testing Data, Using Approximate Bayesian Computation

Publisher： John Wiley & Sons Inc

E-ISSN： 1539-6924|272-4332|9|1595-1610

ISSN： 0272-4332

Source： RISK ANALYSIS, Vol.272-4332, Iss.9, 2015-09, pp. : 1595-1610

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

Related content

Nonparametric Estimation of the Probability of Detection of Flaws in an Industrial Component, from Destructive and Nondestructive Testing Data, Using Approximate Bayesian Computation

By  

RISK ANALYSIS, Vol. 35, Iss. 9, 2015-09 ,pp. : 1595-1610

John Wiley & Sons Inc

Access to resources Recommend Favorite

Advanced Evaluation of Fatigue Phenomena Using Non-Destructive Testing Methods

By  

Materials Science Forum, Vol. 2016, Iss. 879, 2017-02 ,pp. : 1841-1846

Trans Tech Publications

Access to resources Recommend Favorite

Bayesian non‐parametric conditional copula estimation of twin data

By  

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Vol. 67, Iss. 3, 2018-04 ,pp. : 523-548

John Wiley & Sons Inc

Access to resources Recommend Favorite

Non-Destructive Strength Estimation of Lightweight Aggregate Concretes

By  

Materials Science Forum, Vol. 2017, Iss. 885, 2017-03 ,pp. : 104-110

Trans Tech Publications

Access to resources Recommend Favorite

Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion

By Botha E. J.   Leblon B.   Zebarth B. J.   Watmough J.  

International Journal of Remote Sensing, Vol. 31, Iss. 7, 2010-03 ,pp. : 1679-1697

Taylor & Francis Ltd

Access to resources Recommend Favorite