Parameter estimation for reactive transport by a Monte-Carlo approach

AIChE Journal ◽  
2006 ◽  
Vol 52 (6) ◽  
pp. 2281-2289 ◽  
Author(s):  
Mohit Aggarwal ◽  
Jérôme Carrayrou
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Reactive Transport ◽  
Monte Carlo Approach

Reactive transport parameter estimation: Genetic algorithm vs. Monte carlo approach

AIChE Journal ◽  
2009 ◽  
Vol 55 (8) ◽  
pp. 1959-1968 ◽  
Author(s):  
Samer Majdalani ◽  
Marwan Fahs ◽  
Jérôme Carrayrou ◽  
Philippe Ackerer
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Parameter Estimation ◽  
Reactive Transport ◽  
Transport Parameter ◽  
Monte Carlo Approach

Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model

2012 ◽  
Vol 86 (4) ◽  
Author(s):  
Anders Chr. Jensen ◽  
Susanne Ditlevsen ◽  
Mathieu Kessler ◽  
Omiros Papaspiliopoulos
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Approach

Parameter estimation and confidence intervals for Xe-CT ventilation studies: a Monte Carlo approach

1998 ◽  
Vol 84 (2) ◽  
pp. 709-716 ◽  
Author(s):  
Brett A. Simon ◽  
Catherine Marcucci ◽  
Mansheung Fung ◽  
Subhash R. Lele
Keyword(s):  
Computed Tomography ◽  
Monte Carlo ◽  
Parameter Estimation ◽  
Confidence Intervals ◽  
Statistical Significance ◽  
Pulmonary Ventilation ◽  
Repeated Measurements ◽  
Model Parameters ◽  
Monte Carlo Approach ◽  
Ventilation Studies

Simon, Brett A., Catherine Marcucci, Mansheung Fung, and Subhash R. Lele. Parameter estimation and confidence intervals for Xe-CT ventilation studies: a Monte Carlo approach. J. Appl. Physiol. 84(2): 709–716, 1998.—Xenon-enhanced computed tomography (Xe-CT) is a technique for the noninvasive measurement of regional pulmonary ventilation from the washin and/or washout time constants of radiodense stable xenon gas, determined from serial computed tomography scans. Although the measurement itself is straightforward, there is a need for methods for the estimation of variability and confidence intervals so that the statistical significance of the information obtained may be evaluated, particularly since obtaining repeated measurements is often not practical. We present a Monte Carlo (MC) approach to determine the 95% confidence interval (CI) for any given measurement. This MC method was characterized in terms of its unbiasedness and coverage of the CI. In addition, 10 identical Xe-CT ventilation runs were performed in an anesthetized dog, and the time constant was determined for several regions of varying size in each run. The 95% CI, estimated from these repeated measurements as the mean ± 2 × SE, compared favorably with the CI obtained by the MC approach. Finally, a simulation was performed to compare the performance of three imaging protocols in estimating model parameters.

Sign in / Sign up

Export Citation Format

Share Document