scholarly journals A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems

2017 ◽  
Vol 27 (6) ◽  
pp. 063116 ◽  
Author(s):  
Christos Merkatas ◽  
Konstantinos Kaloudis ◽  
Spyridon J. Hatjispyros
Author(s):  
Grace Ashley ◽  
Nii Attoh-Okine

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.


2009 ◽  
Vol 09 (02) ◽  
pp. 205-215 ◽  
Author(s):  
XIANFENG MA ◽  
ERCAI CHEN

The topological pressure is defined for subadditive sequence of potentials in bundle random dynamical systems. A variational principle for the topological pressure is set up in a very weak condition. The result may have some applications in the study of multifractal analysis for random version of nonconformal dynamical systems.


2003 ◽  
Vol 67 (2) ◽  
Author(s):  
Ying-Cheng Lai ◽  
Zonghua Liu ◽  
Lora Billings ◽  
Ira B. Schwartz

Nonlinearity ◽  
2017 ◽  
Vol 30 (7) ◽  
pp. 2835-2853 ◽  
Author(s):  
Anna Maria Cherubini ◽  
Jeroen S W Lamb ◽  
Martin Rasmussen ◽  
Yuzuru Sato

Sign in / Sign up

Export Citation Format

Share Document