Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statistics
Due to advancements in Bayesian modeling, the likelihood-free posterior estimate is now feasible. These estimation methods are crucial for a deeper understanding of simulation-based systems since it might be difficult, if not impossible, to estimate probability values. This compares the effectivenes...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-07-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823004465 |
| _version_ | 1797790846962106368 |
|---|---|
| author | Sadiah M.A. Aljeddani M.A. Mohammed |
| author_facet | Sadiah M.A. Aljeddani M.A. Mohammed |
| author_sort | Sadiah M.A. Aljeddani |
| collection | DOAJ |
| description | Due to advancements in Bayesian modeling, the likelihood-free posterior estimate is now feasible. These estimation methods are crucial for a deeper understanding of simulation-based systems since it might be difficult, if not impossible, to estimate probability values. This compares the effectiveness of such updated estimate approaches to earlier maximum likelihood forecasting models and offers some adjustments to maximum likelihood estimation for estimating the parameters of the Bayesian analysis in this work. Recent improvements in Bayesian modeling have made it feasible to obtain the likelihood-free posterior estimate. These estimate methods are essential for evaluating simulation-based theories since it might be difficult, if not impossible, to determine probability values. Simulation-based concepts such as the Leaky Competing Accumulator (LCA) theory and Feed-Forward Inhibition (FFI) theory have not yet benefited from Bayesian techniques. As assessment criteria, total relative deviation (TRD), total mean square error (TMSE), and Stein Loss Function (SLF) are used. Maximum likelihood (ML) estimates are modified because the original statistic's Cumulative Distribution Function (CDF) is better than traditional ML estimations and other modified estimators emphasize average and coefficient variability. Prior estimator performance was unaffected by response rate or real parameter settings. These systems have not been formally evaluated by the Bayesian factor. |
| first_indexed | 2024-03-13T02:10:20Z |
| format | Article |
| id | doaj.art-a725515fb99b4d78b4281e7a0dba0546 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-13T02:10:20Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-a725515fb99b4d78b4281e7a0dba05462023-07-01T04:34:29ZengElsevierAlexandria Engineering Journal1110-01682023-07-0175221232Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statisticsSadiah M.A. Aljeddani0M.A. Mohammed1Department of Mathematics, Al-Lith University College, Umm Al-Qura University, Al-Lith, Saudi Arabia; Corresponding author at: Department of Mathematics, Al-Lith University College, Umm Al-Qura University, Al-Lith, Saudi Arabia.Department of Mathematics, Al-Lith University College, Umm Al-Qura University, Al-Lith, Saudi Arabia; Department of Mathematics, Faculty of Science, Assiut University, Assiut, EgyptDue to advancements in Bayesian modeling, the likelihood-free posterior estimate is now feasible. These estimation methods are crucial for a deeper understanding of simulation-based systems since it might be difficult, if not impossible, to estimate probability values. This compares the effectiveness of such updated estimate approaches to earlier maximum likelihood forecasting models and offers some adjustments to maximum likelihood estimation for estimating the parameters of the Bayesian analysis in this work. Recent improvements in Bayesian modeling have made it feasible to obtain the likelihood-free posterior estimate. These estimate methods are essential for evaluating simulation-based theories since it might be difficult, if not impossible, to determine probability values. Simulation-based concepts such as the Leaky Competing Accumulator (LCA) theory and Feed-Forward Inhibition (FFI) theory have not yet benefited from Bayesian techniques. As assessment criteria, total relative deviation (TRD), total mean square error (TMSE), and Stein Loss Function (SLF) are used. Maximum likelihood (ML) estimates are modified because the original statistic's Cumulative Distribution Function (CDF) is better than traditional ML estimations and other modified estimators emphasize average and coefficient variability. Prior estimator performance was unaffected by response rate or real parameter settings. These systems have not been formally evaluated by the Bayesian factor.http://www.sciencedirect.com/science/article/pii/S1110016823004465Bayesian AnalysisA moment of order statisticMaximum likelihoodModel parameterCumulative Distribution Function |
| spellingShingle | Sadiah M.A. Aljeddani M.A. Mohammed Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statistics Alexandria Engineering Journal Bayesian Analysis A moment of order statistic Maximum likelihood Model parameter Cumulative Distribution Function |
| title | Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statistics |
| title_full | Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statistics |
| title_fullStr | Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statistics |
| title_full_unstemmed | Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statistics |
| title_short | Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statistics |
| title_sort | parameter estimation of a model using maximum likelihood function and bayesian analysis through moment of order statistics |
| topic | Bayesian Analysis A moment of order statistic Maximum likelihood Model parameter Cumulative Distribution Function |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823004465 |
| work_keys_str_mv | AT sadiahmaaljeddani parameterestimationofamodelusingmaximumlikelihoodfunctionandbayesiananalysisthroughmomentoforderstatistics AT mamohammed parameterestimationofamodelusingmaximumlikelihoodfunctionandbayesiananalysisthroughmomentoforderstatistics |