Assessing Multinomial Distributions with a Bayesian Approach

Luai Al-Labadi (), Petru Ciur, Milutin Dimovic and Kyuson Lim
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Luai Al-Labadi: Department of Mathematical & Computational Sciences, University of Toronto Mississauga, Toronto, ON L5L 1C6, Canada
Petru Ciur: Department of Mathematical & Computational Sciences, University of Toronto Mississauga, Toronto, ON L5L 1C6, Canada
Milutin Dimovic: Department of Mathematical & Computational Sciences, University of Toronto Mississauga, Toronto, ON L5L 1C6, Canada
Kyuson Lim: Department of Mathematics & Statistics, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada

Mathematics, 2023, vol. 11, issue 13, 1-16

Abstract: This paper introduces a unified Bayesian approach for testing various hypotheses related to multinomial distributions. The method calculates the Kullback–Leibler divergence between two specified multinomial distributions, followed by comparing the change in distance from the prior to the posterior through the relative belief ratio. A prior elicitation algorithm is used to specify the prior distributions. To demonstrate the effectiveness and practical application of this approach, it has been applied to several examples.

Keywords: dirichlet distribution; hypothesis testing; Kullback–Leibler divergence; multinomial distribution; relative belief inferences (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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