Variational Bayesian approach for interval estimation of NHPP-based software reliability models
Okamura H, Grottke M, Dohi T, Trivedi K (2007)
Publication Type: Conference contribution, Original article
Publication year: 2007
Edited Volumes: Proceedings of the International Conference on Dependable Systems and Networks
Pages Range: 698-707
Event location: Edinburgh
DOI: 10.1109/DSN.2007.101
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. © 2007 IEEE.
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APA:
Okamura, H., Grottke, M., Dohi, T., & Trivedi, K. (2007). Variational Bayesian approach for interval estimation of NHPP-based software reliability models. In Proceedings of the International IEEE Conference on Dependable Systems and Networks 2007 (pp. 698-707). Edinburgh.
MLA:
Okamura, Hiroyuki, et al. "Variational Bayesian approach for interval estimation of NHPP-based software reliability models." Proceedings of the International IEEE Conference on Dependable Systems and Networks 2007, Edinburgh 2007. 698-707.
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