A0686
Title: Bayesian mixture modeling using a mixture of finite mixtures with normalized inverse Gaussian weights
Authors: Fumiya Iwashige - Graduate School of Advanced Science and Engineering, Hiroshima University (Japan) [presenting]
Abstract: In Bayesian inference for mixture models with an unknown number of components, a finite mixture model is usually employed that assumes prior distributions for mixing weights and the number of components. This model is called a mixture of finite mixtures (MFM). The focus is on estimating the number of components. As a robust alternative to Dirichlet weights, a method based on a mixture of finite mixtures with normalized inverse Gaussian weights is presented. The motivation is similar to the use of normalized inverse Gaussian processes instead of Dirichlet processes for infinite mixture modeling. Introducing latent variables, the posterior computation is carried out using block Gibbs sampling without using the reversible jump algorithm. The performance of the proposed method is illustrated through some numerical experiments and real data examples, including clustering, density estimation, and community detection.
