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Bayesian Mixture Models with Weight-Dependent Component Priors for Bayesian Clustering

Oftadeh, Elaheh and Zhang, Jian (2019) Bayesian Mixture Models with Weight-Dependent Component Priors for Bayesian Clustering. In: The Festschrift in Honour of Professor Kai-Tai Fang's 80 Birthday. Springer, pp. 1-15. (Unpublished)

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Abstract

In the conventional Bayesian mixture models, independent priors are often assigned to weights and component parameters. This may cause bias in estimation of missing group memberships due to the domination of these priors for some components when there is a big variation across component weights. To tackle this issue, we propose weight-dependent priors for component parameters. To implement the proposal, we develop a simple coordinate-wise updating algorithm for finding empirical Bayesian estimator of allocation or labelling vector of observations. We conduct a simulation study to show that the new method can outperform the existing approaches in terms of adjusted Rand index.

Item Type: Book section
Uncontrolled keywords: Finite mixture models, Weight-dependent priors, adjusted RAND index and cluster analysis.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 01 May 2019 09:57 UTC
Last Modified: 03 Jun 2019 09:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/73712 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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