Zhou, Shirong, Xu, Ancha, Tang, Yincai, and Shen, Lijuan (2024) Fast Bayesian Inference of Reparameterized Gamma Process With Random Effects. IEEE Transactions on Reliability, 73 (1). pp. 399-412.

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Abstract

In the field of reliability engineering, the gamma process plays an important role in modeling degradation processes. However, extracting lifetime information from product degradation observations has long been suffering from both ineffective modeling techniques and inefficient statistical inference methods. To overcome these challenges, we propose a reparameterized gamma process with random effects in this article. Compared with the classical gamma process, the proposed model has a more intuitive physical interpretation. In addition, statistical inference for the model can be readily done through the variational Bayesian algorithm. Combining with the Gauss–Hermite quadrature and the Laplace approximation, the algorithm yields closed-form variational posteriors for the proposed model. Its superiority over two other inference methods (expectation maximization and Monte Carlo Markov Chain) in terms of computational efficiency and estimation accuracy is demonstrated by simulation.

Item ID:	93251
Item Type:	Article (Research - C1)
ISSN:	1558-1721
Copyright Information:	© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Date Deposited:	07 Aug 2026 05:57
FoR Codes:	49 MATHEMATICAL SCIENCES > 4905 Statistics > 490510 Stochastic analysis and modelling @ 50% 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490508 Statistical data science @ 20% 40 ENGINEERING > 4010 Engineering practice and education > 401006 Systems engineering @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 25% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280118 Expanding knowledge in the mathematical sciences @ 60% 24 MANUFACTURING > 2404 Computer, electronic and communication equipment > 240403 Integrated circuits and devices @ 15%
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