Probabilistic model forecasting for rail wear in seoul metro based on bayesian theory

[1]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[2]  G. Kitagawa.  A self-organizing state-space model , 1998 .

[3]  Achintya Haldar,et al.  Probability, Reliability and Statistical Methods in Engineering Design (Haldar, Mahadevan) , 1999 .

[4]  Roman Krzysztofowicz,et al.  Bayesian theory of probabilistic forecasting via deterministic hydrologic model , 1999 .

[5]  Masashi Miwa.  Mathematical Programming Model Analysis for the Optimal Track Maintenance Schedule , 2002 .

[6]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[7]  Masashi Miwa,et al.  Actual Data Analysis of Alignment Irregularity Growth and its Prediction Model , 2005 .

[8]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[9]  Wilson H. Tang,et al.  Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering , 2006 .

[10]  Jung Hoon Kim,et al.  Evaluation of the Railroad Track Life Cycle Based on the Metro Rail Wear Data Regression Analysis , 2010 .

[11]  You Ling,et al.  Stochastic prediction of fatigue loading using real-time monitoring data , 2011 .

[12]  Michael Dowd,et al.  Estimating behavioral parameters in animal movement models using a state-augmented particle filter. , 2011, Ecology.

[13]  Hong-Zhong Huang,et al.  Probabilistic Low Cycle Fatigue Life Prediction Using an Energy-Based Damage Parameter and Accounting for Model Uncertainty , 2012 .

[14]  G. Zi,et al.  Probabilistic prognosis of fatigue crack growth for asphalt concretes , 2015 .