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Article

A Symmetrical Analysis of Decision Making: Introducing the Gaussian Negative Binomial Mixture with a Latent Class Choice Model

by
Irsa Sajjad
1,
Ibrahim Ali Nafisah
2,
Mohammed M. A. Almazah
3,
Osama Abdulaziz Alamri
4 and
Javid Gani Dar
5,*
1
School of Mathematics and Statistics, Central South University, Changsha 410083, China
2
Department of Statistics and Operations Research, College of Sciences, King Saud University, P.O.Box 2454, Riyadh 11451, Saudi Arabia
3
Department of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil 61421, Saudi Arabia
4
Statistics Department, Faculty of Science, University of Tabuk, Tabuk 47512, Saudi Arabia
5
Department of Applied Sciences, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India
*
Author to whom correspondence should be addressed.
Symmetry 2024, 16(7), 908; https://doi.org/10.3390/sym16070908 (registering DOI)
Submission received: 11 June 2024 / Revised: 8 July 2024 / Accepted: 10 July 2024 / Published: 16 July 2024
(This article belongs to the Special Issue Symmetric or Asymmetric Distributions and Its Applications)

Abstract

This research presents a model called the ‘Gaussian negative binomial mixture with a latent class choice model’, which serves as a robust and efficient tool for analyzing decisions across different areas. Our innovative model combines elements of mixture models, negative binomial distributions, and latent class choice modeling to create an approach that captures the complexities of decision-making processes. We explain how the model is formulated and estimated, showcasing its effectiveness in analyzing and predicting choices in scenarios. Through the use of a dataset, we demonstrate the performance of this method, marking a significant advancement in choice modeling. Our results highlight the applications of this model and point towards promising directions for future research, especially in exploring symmetrical patterns and structures, within decision-making processes.
Keywords: unsupervised machine learning; Gaussian mixture model; latent class choice model; negative binomial mixture model; indoor environmental quality; thermal comfort models unsupervised machine learning; Gaussian mixture model; latent class choice model; negative binomial mixture model; indoor environmental quality; thermal comfort models

Share and Cite

MDPI and ACS Style

Sajjad, I.; Nafisah, I.A.; M. A. Almazah, M.; Alamri, O.A.; Dar, J.G. A Symmetrical Analysis of Decision Making: Introducing the Gaussian Negative Binomial Mixture with a Latent Class Choice Model. Symmetry 2024, 16, 908. https://doi.org/10.3390/sym16070908

AMA Style

Sajjad I, Nafisah IA, M. A. Almazah M, Alamri OA, Dar JG. A Symmetrical Analysis of Decision Making: Introducing the Gaussian Negative Binomial Mixture with a Latent Class Choice Model. Symmetry. 2024; 16(7):908. https://doi.org/10.3390/sym16070908

Chicago/Turabian Style

Sajjad, Irsa, Ibrahim Ali Nafisah, Mohammed M. A. Almazah, Osama Abdulaziz Alamri, and Javid Gani Dar. 2024. "A Symmetrical Analysis of Decision Making: Introducing the Gaussian Negative Binomial Mixture with a Latent Class Choice Model" Symmetry 16, no. 7: 908. https://doi.org/10.3390/sym16070908

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