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