A NEW PROCEDURE : BAYESIAN SELECTION TO FIND THE BEST OF GEOMETRIC POPULATION UNDER GENERAL LOSS FUNCTION
In many practical situations the experimenter is confronted with the problem of choosing the best one of a number of populations or categories or ranking them according to their performance . This paper derives a procedure for selecting the better of Two Geometric populations employing a decision-t...
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| Format: | Article |
| Language: | English |
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Faculty of Computer Science and Mathematics, University of Kufa
2012-12-01
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| Series: | Journal of Kufa for Mathematics and Computer |
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| Online Access: | https://journal.uokufa.edu.iq/index.php/jkmc/article/view/2177 |
| _version_ | 1797266198721724416 |
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| author | Samira Faisal Hathoot |
| author_facet | Samira Faisal Hathoot |
| author_sort | Samira Faisal Hathoot |
| collection | DOAJ |
| description |
In many practical situations the experimenter is confronted with the problem of choosing the best one of a number of populations or categories or ranking them according to their performance . This paper derives a procedure for selecting the better of Two Geometric populations employing a decision-theoretic Bayesian framework with Beta prior under general loss function .
the numerical results for this procedure are given by using Math Works Matlab ver 7.0.1 with different loss functions constant , linear and quadratic , where in one equation we can obtain the Bayes risk for the three types of the loss functions : constant , linear and quadratic .
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| first_indexed | 2024-04-25T00:56:53Z |
| format | Article |
| id | doaj.art-2ed53a885ef947a3bc824d79c5f871a1 |
| institution | Directory Open Access Journal |
| issn | 2076-1171 2518-0010 |
| language | English |
| last_indexed | 2024-04-25T00:56:53Z |
| publishDate | 2012-12-01 |
| publisher | Faculty of Computer Science and Mathematics, University of Kufa |
| record_format | Article |
| series | Journal of Kufa for Mathematics and Computer |
| spelling | doaj.art-2ed53a885ef947a3bc824d79c5f871a12024-03-11T09:53:17ZengFaculty of Computer Science and Mathematics, University of KufaJournal of Kufa for Mathematics and Computer2076-11712518-00102012-12-011610.31642/JoKMC/2018/010606A NEW PROCEDURE : BAYESIAN SELECTION TO FIND THE BEST OF GEOMETRIC POPULATION UNDER GENERAL LOSS FUNCTIONSamira Faisal Hathoot In many practical situations the experimenter is confronted with the problem of choosing the best one of a number of populations or categories or ranking them according to their performance . This paper derives a procedure for selecting the better of Two Geometric populations employing a decision-theoretic Bayesian framework with Beta prior under general loss function . the numerical results for this procedure are given by using Math Works Matlab ver 7.0.1 with different loss functions constant , linear and quadratic , where in one equation we can obtain the Bayes risk for the three types of the loss functions : constant , linear and quadratic . https://journal.uokufa.edu.iq/index.php/jkmc/article/view/2177T- openl- continuous |
| spellingShingle | Samira Faisal Hathoot A NEW PROCEDURE : BAYESIAN SELECTION TO FIND THE BEST OF GEOMETRIC POPULATION UNDER GENERAL LOSS FUNCTION Journal of Kufa for Mathematics and Computer T- open l- continuous |
| title | A NEW PROCEDURE : BAYESIAN SELECTION TO FIND THE BEST OF GEOMETRIC POPULATION UNDER GENERAL LOSS FUNCTION |
| title_full | A NEW PROCEDURE : BAYESIAN SELECTION TO FIND THE BEST OF GEOMETRIC POPULATION UNDER GENERAL LOSS FUNCTION |
| title_fullStr | A NEW PROCEDURE : BAYESIAN SELECTION TO FIND THE BEST OF GEOMETRIC POPULATION UNDER GENERAL LOSS FUNCTION |
| title_full_unstemmed | A NEW PROCEDURE : BAYESIAN SELECTION TO FIND THE BEST OF GEOMETRIC POPULATION UNDER GENERAL LOSS FUNCTION |
| title_short | A NEW PROCEDURE : BAYESIAN SELECTION TO FIND THE BEST OF GEOMETRIC POPULATION UNDER GENERAL LOSS FUNCTION |
| title_sort | new procedure bayesian selection to find the best of geometric population under general loss function |
| topic | T- open l- continuous |
| url | https://journal.uokufa.edu.iq/index.php/jkmc/article/view/2177 |
| work_keys_str_mv | AT samirafaisalhathoot anewprocedurebayesianselectiontofindthebestofgeometricpopulationundergenerallossfunction AT samirafaisalhathoot newprocedurebayesianselectiontofindthebestofgeometricpopulationundergenerallossfunction |