scholarly journals Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

2018 ◽  
Vol 118 ◽  
pp. 143-158 ◽  
Author(s):  
Eric Bradford ◽  
Artur M. Schweidtmann ◽  
Dongda Zhang ◽  
Keju Jing ◽  
Ehecatl Antonio del Rio-Chanona
Keyword(s):  
Dynamic Modeling ◽  
Gaussian Processes ◽  
Algal Production ◽  
Modeling And Optimization ◽  
Multivariate Gaussian

Dynamic Modeling and Optimization of Micro-Hotplate Chemical Gas Sensors

1997 ◽  
Vol 30 (9) ◽  
pp. 91-95 ◽  
Author(s):  
Tekin Kunt ◽  
Thomas J. McAvoy ◽  
Richard E. Cavicchi ◽  
Steve Semancik
Keyword(s):  
Dynamic Modeling ◽  
Gas Sensors ◽  
Modeling And Optimization

Dynamic Modeling and Optimization of Batch Crystallization Processes

1999 ◽  
Vol 32 (2) ◽  
pp. 6621-6626
Author(s):  
Yi-Dong Lang ◽  
Arturo M. Cervantes ◽  
Lorenz T. Biegler
Keyword(s):  
Dynamic Modeling ◽  
Batch Crystallization ◽  
Modeling And Optimization

Dynamic Modeling and Optimization for Vibration Control of a Composite Cantilever With Magnetostrictive Shunt Damper

2018 ◽  
Vol 54 (11) ◽  
pp. 1-6
Author(s):  
Jiaju Zheng ◽  
Shuying Cao ◽  
Ruzheng Pan ◽  
Shuyu Cao ◽  
Guiying Song ◽  
...  
Keyword(s):  
Vibration Control ◽  
Dynamic Modeling ◽  
Modeling And Optimization

scholarly journals Joint segmentation of multivariate Gaussian processes using mixed linear models

2011 ◽  
Vol 55 (2) ◽  
pp. 1160-1170 ◽  
Author(s):  
F. Picard ◽  
E. Lebarbier ◽  
E. Budinskà ◽  
S. Robin
Keyword(s):  
Gaussian Processes ◽  
Linear Models ◽  
Mixed Linear Models ◽  
Multivariate Gaussian

scholarly journals Generalized Inferences about the Mean Vector of Several Multivariate Gaussian Processes

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pilar Ibarrola ◽  
Ricardo Vélez
Keyword(s):  
Gaussian Processes ◽  
Confidence Region ◽  
Confidence Regions ◽  
Covariance Matrices ◽  
Nuisance Parameters ◽  
Vector Parameter ◽  
Multivariate Gaussian ◽  
The Mean ◽  
Unknown Vector ◽  
Mean Vector

We consider in this paper the problem of comparing the means of several multivariate Gaussian processes. It is assumed that the means depend linearly on an unknown vector parameterθand that nuisance parameters appear in the covariance matrices. More precisely, we deal with the problem of testing hypotheses, as well as obtaining confidence regions forθ. Both methods will be based on the concepts of generalizedpvalue and generalized confidence region adapted to our context.

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