Use of Posterior Probabilities to Evaluate Methods of Discriminant Analysis

1981 ◽  
Vol 20 (04) ◽  
pp. 207-212 ◽  
Author(s):  
J. Hermans ◽  
B. van Zomeren ◽  
J. W. Raatgever ◽  
P. J. Sterk ◽  
J. D. F. Habbema
Keyword(s):  
Experimental Data ◽  
Discriminant Analysis ◽  
Posterior Probabilities

By means of a case study the choice between several methods of discriminant analysis is presented. Experimental data of a two-groups problem with one or two variables is analysed. The different methods are compared according to posterior probabilities which can be computed for each subject and which are the basis of discriminant analysis. These posterior probabilities are analysed graphically as well as numerically.

Effects of Over Fire Air on the Combustion and NOx Emission Characteristics of a 600 MW Opposed Swirling Fired Boiler

2012 ◽  
Vol 512-515 ◽  
pp. 2135-2142 ◽  
Author(s):  
Yu Peng Wu ◽  
Zhi Yong Wen ◽  
Yue Liang Shen ◽  
Qing Yan Fang ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Experimental Data ◽  
Empirical Method ◽  
Nox Emission ◽  
Emission Characteristics ◽  
Cfd Model ◽  
Nitrogen Release ◽  
Computational Fluid Dynamics Cfd ◽  
Low Nox Emission

A computational fluid dynamics (CFD) model of a 600 MW opposed swirling coal-fired utility boiler has been established. The chemical percolation devolatilization (CPD) model, instead of an empirical method, has been adapted to predict the nitrogen release during the devolatilization. The current CFD model has been validated by comparing the simulated results with the experimental data obtained from the boiler for case study. The validated CFD model is then applied to study the effects of ratio of over fire air (OFA) on the combustion and nitrogen oxides (NOx) emission characteristics. It is found that, with increasing the ratio of OFA, the carbon content in fly ash increases linearly, and the NOx emission reduces largely. The OFA ratio of 30% is optimal for both high burnout of pulverized coal and low NOx emission. The present study provides helpful information for understanding and optimizing the combustion of the studied boiler

scholarly journals Simultaneous Parameters Identifiability and Estimation of anE. coliMetabolic Network Model

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Kese Pontes Freitas Alberton ◽  
André Luís Alberton ◽  
Jimena Andrea Di Maggio ◽  
Vanina Gisela Estrada ◽  
María Soledad Díaz ◽  
...  
Keyword(s):  
Experimental Data ◽  
Escherichia Coli ◽  
Metabolic Networks ◽  
Initial Conditions ◽  
Model Fit ◽  
Real Problem ◽  
Kinetic Rates ◽  
K 12 ◽  
Procedure Model

This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of theEscherichia coliK-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

ON THE DISJOINTNESS OF ALGEBRAICALLY CONSTRUCTED COSTAS ARRAYS

2011 ◽  
Vol 10 (02) ◽  
pp. 219-240 ◽  
Author(s):  
KONSTANTINOS DRAKAKIS ◽  
ROD GOW ◽  
SCOTT RICKARD
Keyword(s):  
Experimental Data

Is it possible for a particular Costas array to be generated by two different constructions of the Golomb and Welch families? Experimental data suggests that this does not happen (except for trivially small orders), and a (partial) proof of this fact is offered herein through a case-by-case study of all possible pairs of constructions that can potentially produce Costas arrays of equal order.

Do all roads lead to Rome?: Modeling register variation with factor analysis and discriminant analysis

2018 ◽  
Vol 14 (2) ◽  
pp. 233-273 ◽  
Author(s):  
Jesse Egbert ◽  
Douglas Biber
Keyword(s):  
Factor Analysis ◽  
Discriminant Analysis ◽  
Canonical Discriminant Analysis ◽  
Statistical Techniques ◽  
Systematic Relationship ◽  
Case Study Analysis ◽  
Register Variation ◽  
Analysis Of Variation ◽  
Occurrence Patterns

Abstract Previous theoretical and empirical research on register variation has argued that linguistic co-occurrence patterns have a highly systematic relationship to register differences, because they both share the same functional underpinnings. The goal of this study is to test this claim through a comparison of two statistical techniques that have been used to describe register variation: factor analysis (as used in Multi-Dimensional analysis, MDA) and canonical discriminant analysis (CDA). MDA and CDA have different statistical bases and thus give priority to different analytical considerations: linguistic co-occurrence in the case of MDA and the prediction of register differences in the case of CDA. Thus, there is no statistical reason to expect that the two techniques, if applied to the same corpus, will produce similar results. We hypothesize that although MDA and CDA approach register variation from opposite sides, they will produce similar results because both types of statistical patterns are motivated by underlying discourse functions. The present paper tests this claim through a case-study analysis of variation among web registers, applying MDA and CDA to analyze register variation in the same corpus of texts.

Coupling Proton Transfer Reaction−Mass Spectrometry with Linear Discriminant Analysis:  a Case Study

2003 ◽  
Vol 51 (25) ◽  
pp. 7227-7233 ◽  
Author(s):  
Franco Biasioli ◽  
Flavia Gasperi ◽  
Eugenio Aprea ◽  
Daniela Mott ◽  
Elena Boscaini ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Proton Transfer ◽  
Linear Discriminant Analysis ◽  
Transfer Reaction ◽  
Proton Transfer Reaction ◽  
Reaction Mass ◽  
Linear Discriminant
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