Bayesian non-parametric simulation of hazard functions

Belyi,; Popova,; Morton, David P.; Damien,

doi:10.1109/wsc.2009.5429359

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the survival analysis employs Bayesian nonparametric models to evaluate the proportional hazard function [20]. Moreover, an adapted version of MCMC algorithm has been used to simulate Bayesian nonparametric models for hazard functions [12] [21].…”

Section: A Nonparametric Bayesian Approachmentioning

confidence: 99%

Bayesian nonparametric reliability analysis for a railway system at component level

Mokhtarian

Namzi-Rad

et al. 2013

2013 IEEE International Conference on Intelligent Rail Transportation Proceedings

View full text Add to dashboard Cite

Railway system is a typical large-scale complex system with interconnected sub-systems which contain numerous components. System reliability is retained through appropriate maintenance measures and cost-effective asset management requires accurate estimation of reliability at the lowest level. However, real-life reliability data at component level of a railway system is not always available in practice, let alone complete. The component lifetime distributions from the manufacturers are often obscured and complicated by the actual usage and working environments. Reliability analysis thus calls for a suitable methodology to estimate a component lifetime under the conditions of a lack of failure data and unknown and/or mixture lifetime distributions. This paper proposes a nonparametric Bayesian approach with a Dirichlet Process Mixture Model (DPMM) to facilitate reliability analysis in a railway system. Simulation results will be given to illustrate the effectiveness of the proposed approach in lifetime estimation.

show abstract

Section: A Nonparametric Bayesian Approachmentioning

confidence: 99%