DocumentCode :
3056409
Title :
Variational Bayesian Approach for Interval Estimation of NHPP-Based Software Reliability Models
Author :
Okamura, Hiroyuki ; Grottke, Michael ; Dohi, Tadashi ; Trivedi, Kishor S.
Author_Institution :
Hiroshima Univ., Hiroshima
fYear :
2007
fDate :
25-28 June 2007
Firstpage :
698
Lastpage :
707
Abstract :
In this paper, we present a variational Bayesian (VB) approach to computing the interval estimates for nonhomogeneous Poisson process (NHPP) software reliability models. This approach is an approximate method that can produce analytically tractable posterior distributions. We present simple iterative algorithms to compute the approximate posterior distributions for the parameters of the gamma-type NHPP-based software reliability model using either individual failure time data or grouped data. In numerical examples, the accuracy of this VB approach is compared with the interval estimates based on conventional Bayesian approaches, i.e., Laplace approximation, Markov chain Monte Carlo (MCMC) method, and numerical integration. The proposed VB approach provides almost the same accuracy as MCMC, while its computational burden is much lower.
Keywords :
Poisson distribution; iterative methods; software reliability; approximate posterior distributions; interval estimation; iterative algorithms; nonhomogeneous Poisson process; software reliability models; variational Bayesian approach; Bayesian methods; Context modeling; Delay; Distributed computing; Finite wordlength effects; Iterative algorithms; Monte Carlo methods; Reliability engineering; Software reliability; Statistical analysis; Software reliability; interval estimation; non-homogeneous Poisson process; variational Bayes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Dependable Systems and Networks, 2007. DSN '07. 37th Annual IEEE/IFIP International Conference on
Conference_Location :
Edinburgh
Print_ISBN :
0-7695-2855-4
Type :
conf
DOI :
10.1109/DSN.2007.101
Filename :
4273021
Link To Document :
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