Identification of the parameters of complex constitutive models: Least squares minimization vs. Bayesian updating

2010 ◽  
pp. 131-142 ◽  
Keyword(s):  
Least Squares ◽  
Constitutive Models ◽  
Bayesian Updating ◽  
Least Squares Minimization

scholarly journals Solvability classes for core problems in matrix total least squares minimization

2019 ◽  
Vol 64 (2) ◽  
pp. 103-128 ◽  
Author(s):  
Iveta Hnětynková ◽  
Martin Plešinger ◽  
Jana Žáková
Keyword(s):  
Least Squares ◽  
Total Least Squares ◽  
Least Squares Minimization

A new least-squares minimization approach to depth and shape determination from magnetic data

2007 ◽  
Vol 55 (3) ◽  
pp. 433-446 ◽  
Author(s):  
El-Sayed M. Abdelrahman ◽  
Eid. R. Abo-Ezz ◽  
Khalid S. Essa ◽  
T.M. El-Araby ◽  
Khaled S. Soliman
Keyword(s):  
Least Squares ◽  
Magnetic Data ◽  
Shape Determination ◽  
Minimization Approach ◽  
Least Squares Minimization

scholarly journals Analysis of the hydrogen-rich magnetic White Dwarfs in the SDSS

2008 ◽  
Vol 4 (S259) ◽  
pp. 379-380
Author(s):  
Baybars Külebi ◽  
Stefan Jordan ◽  
Fabian Euchner ◽  
Heiko Hirsch
Keyword(s):  
Magnetic Field ◽  
Least Squares ◽  
White Dwarfs ◽  
Optical Spectra ◽  
Digital Survey ◽  
Least Squares Minimization ◽  
Minimization Scheme ◽  
Field Strengths

AbstractWe have calculated optical spectra of hydrogen-rich (DA) white dwarfs with magnetic field strengths between 1 MG and 1000 MG for temperatures between 7000 K and 50000 K. Through a least-squares minimization scheme, we have analyzed the spectra of 114 magnetic DAs from the Sloan Digital Survey (SDSS; 95 previously published plus 14 newly discovered within SDSS).

A least‐squares minimization approach to depth determination from moving average residual gravity anomalies

Geophysics ◽  
1993 ◽  
Vol 58 (12) ◽  
pp. 1779-1784 ◽  
Author(s):  
El‐Sayed M. Abdelrahman ◽  
Tarek M. El‐Araby
Keyword(s):  
Least Squares ◽  
Gravity Data ◽  
Moving Average ◽  
Gravity Anomalies ◽  
The United States ◽  
Random Errors ◽  
Minimization Method ◽  
Residual Gravity ◽  
Average Residual ◽  
Least Squares Minimization

We have developed a least‐squares minimization method to estimate the depth of a buried structure from moving average residual gravity anomalies. The method involves fitting simple models convolved with the same moving average filter as applied to the observed gravity data. As a result, our method can be applied not only to residuals but also to the Bouguer gravity data of a short profile length. The method is applied to synthetic data with and without random errors. The validity of the method is tested in detail on two field examples from the United States and Senegal.

scholarly journals CAPOW: a standalone program for the calculation of optimal weighting parameters for least-squares crystallographic refinements

2018 ◽  
Vol 51 (1) ◽  
pp. 200-204
Author(s):  
Natalie T. Johnson ◽  
Holger Ott ◽  
Michael R. Probert
Keyword(s):  
Least Squares ◽  
Weighting Scheme ◽  
Optimal Parameters ◽  
Rigorous Analysis ◽  
Single Crystal Diffraction ◽  
Optimal Weights ◽  
Optimal Weighting ◽  
Multipole Refinement ◽  
Precision And Accuracy ◽  
Least Squares Minimization

The rigorous analysis of crystallographic models, refined through the use of least-squares minimization, is founded on the expectation that the data provided have a normal distribution of residuals. Processed single-crystal diffraction data rarely exhibit this feature without a weighting scheme being applied. These schemes are designed to reflect the precision and accuracy of the measurement of observed reflection intensities. While many programs have the ability to calculate optimal parameters for applied weighting schemes, there are still programs that do not contain this functionality, particularly when moving beyond the spherical atom model. For this purpose,CAPOW(calculation and plotting of optimal weights), a new program for the calculation of optimal weighting parameters for aSHELXLweighting scheme, is presented and an example of its application in a multipole refinement is given.

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