A Nonparametric Bayesian Framework for Multivariate Libby-Novick Beta Mixture Models
Niloufar Samiee, Narges Manouchehri, Nizar Bouguila · 2024
This work presents a nonparametric Bayesian approach that utilizes a mixture of multivariate Libby-Novick Beta distributions to address clustering challenges. When using mixtures, model selection is a significant obstacle. As a solution to this problem, we extend the finite Libby-Novick Beta mixture model (FLNBMM)to the infinite case. This enables us to accurately represent the data distribution by accommodating an unspecified number of mixture components. We develop a Bayesian inference strategy based on Markov Chain Monte Carlo to estimate the posterior distribution, which provides strong power and flexibility for modeling and analyzing complicated data. Our suggested method’s effectiveness is assessed on three applications and contrasted with that of FLNBMM, the infinite Gaussian mixture model (IGMM), and the finite Gaussian mixture model (FGMM) to show the efficacy of our methodology. It is evident from the results that our proposed model is a good alternative.