Graph metrics as summary statistics for Approximate Bayesian Computation with application to network model parameter estimation

2014 ◽  
Vol 3 (1) ◽  
pp. 52-83 ◽  
Author(s):  
D. Fay ◽  
A. W. Moore ◽  
K. Brown ◽  
M. Filosi ◽  
G. Jurman
Keyword(s):  
Parameter Estimation ◽  
Network Model ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Summary Statistics ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Graph Metrics ◽  
Approximate Bayesian

Approximate Bayesian computation for railway track geometry parameter estimation

2020 ◽  
pp. 095440972097772
Author(s):  
Grace Ashley ◽  
Nii Attoh-Okine
Keyword(s):  
Parameter Estimation ◽  
Approximate Bayesian Computation ◽  
Railway Track ◽  
Ride Quality ◽  
Bayesian Computation ◽  
Summary Statistics ◽  
Track Geometry ◽  
Degradation Models ◽  
Approximate Bayesian ◽  
Geometry Parameter

The quality of track geometry is directly linked to vehicle safety, reliability and ride quality. The performance of track is therefore considerably hindered when track geometry indicators deviate from the specified and approved limits due to loads and continuous usage. Information obtained from the analysis of track geometry data can inform the prompt application of preventive and corrective maintenance measures like tamping, to increase the lifespan of the track and provide higher train speeds, optimizing track performance. Recently, there has been the application of Bayesian statistical methods in track degradation models. However, most models rely heavily on likelihood functions which are intractable. The aim of this paper is to apply Approximate Bayesian Computation (ABC), also known as the likelihood-free method, in estimating Track Quality Indices (TQIs) which are essential for track degradation modeling. ABC is applied using methods like the rejection algorithm and Markov Chain Monte Carlo (MCMC). In ABC, it is essential that summary statistics are computed from the observed data followed by the simulation of summary statistics for different parameter values. Two ABC-MCMC algorithms were used for parameter estimation in this paper. Models were developed using 200 ft. TQIs and 150 ft. TQIs with different priors and model choice was performed with the aid of Bayes Factors.

Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation

2021 ◽  
Vol 62 (2) ◽  
Author(s):  
Jason D. Christopher ◽  
Olga A. Doronina ◽  
Dan Petrykowski ◽  
Torrey R. S. Hayden ◽  
Caelan Lapointe ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Absorption Spectroscopy ◽  
Approximate Bayesian Computation ◽  
Flow Parameter ◽  
Bayesian Computation ◽  
Laser Absorption Spectroscopy ◽  
Laser Absorption ◽  
Approximate Bayesian
Sign in / Sign up

Export Citation Format

Share Document