Estimation of Failure Probability and Its Applications in Lifetime Data Analysis | Zendy

Han Ming | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Estimation of Failure Probability and Its Applications in Lifetime Data Analysis

Author(s) -

Han Ming

Publication year - 2011

Publication title -

journal of quality and reliability engineering

Language(s) - English

Resource type - Journals

eISSN - 2314-8047

pISSN - 2314-8055

DOI - 10.1155/2011/719534

Subject(s) - markov chain monte carlo , bayesian probability , hyperparameter , bayesian hierarchical modeling , computer science , bayesian average , bayes estimator , bayesian statistics , variable order bayesian network , bayesian linear regression , markov chain , bayesian inference , algorithm , artificial intelligence , machine learning

Since Lindley and Smith introduced the idea of hierarchical prior distribution, someresults have been obtained on hierarchical Bayesian method to deal with lifetime data.But all those results obtained by means of hierarchical Bayesian methods involvecomplicated integration compute. Though some computing methods such as MarkovChain Monte Carlo (MCMC) are available, doing integration is still very inconvenientfor practical problems. This paper introduces a new method, named E-Bayesianestimation method, to estimate failure probability. In the case of one hyperparameter,the definition of E-Bayesian estimation of the failure probability is provided; moreover,the formulas of E-Bayesian estimation and hierarchical Bayesian estimation and theproperty of E-Bayesian estimation of the failure probability are also provided. Finally,calculation on practical problems shows that the provided method is feasible and easyto perform

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research