vimixr: Collapsed Variational Inference for Dirichlet Process (DP) Mixture Model

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Authors: Annesh Pal, Boris P. Hejblum
Cited by: 0

Abstract

Collapsed Variational Inference for a Dirichlet Process (DP) mixture model with unknown covariance matrix structure and DP concentration parameter. It enables efficient clustering of high-dimensional data with significantly improved computational speed than traditional MCMC methods. The package incorporates 8 parameterisations and corresponding prior choices for the unknown covariance matrix, from which the user can choose and apply accordingly.

Fields of Study & Subjects

Bayesian Methods and Mixture Models Gaussian Processes and Bayesian Inference Advanced Clustering Algorithms Research

Publication details

Publication date
12 January 2026
Document type
Dataset