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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
A data application is given.
📌 3 Key Takeaways
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Objective & Hypothesis:The Gauss hypergeometric distribution with the density proportional to x α − 1 (1 − x ) β − 1 (1 + ξx ) − γ , 0 < x < 1 arises in connection with the prior distribution of the parameter ρ (0 < ρ < 1) representing traffic intensity in a M/M/ 1 queue system.
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Key Findings & Data:In this article, we define and study a multivariate generalization of this distribution and derive some of its properties like marginal densities, joint moments, and factorizations.
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Conclusion & Impact:A data application is given.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:beta function · dirichlet function · gamma function
Abstract
The Gauss hypergeometric distribution with the density proportional to x α − 1 (1 − x ) β − 1 (1 + ξx ) − γ , 0 < x < 1 arises in connection with the prior distribution of the parameter ρ (0 < ρ < 1) representing traffic intensity in a M/M/ 1 queue system. In this article, we define and study a multivariate generalization of this distribution and derive some of its properties like marginal densities, joint moments, and factorizations. A data application is given.
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Cite This Article
Nagar, D. K., Bedoya-valencia, D., Nadarajah, S. (2015). Multivariate generalization of the Gauss hypergeometric distribution. Hacettepe Journal of Mathematics and Statistics, Volume: 44 Issue: 4. https://ajindex.com/articles/1403271