# Modeling with Normalized Random Measure Mixture Models

arXiv: https://arxiv.org/abs/1310.0260 · HTML: https://mlsanity.com/paper/1310.0260/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2013.

## Built on by (most cited first, 3 of 3)

- [Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics](https://mlsanity.com/paper/2107.03584/) (2021)
- [Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data](https://mlsanity.com/paper/2302.09111/) (2023)
- [Scalable Posterior Uncertainty for Flexible Density-Based Clustering](https://mlsanity.com/paper/2603.03188/) (2026)

## Abstract (arXiv)

The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimation and clustering. The goal of this paper is to illustrate the use of normalized random measures as mixing measures in nonparametric hierarchical mixture models and point out how possible computational issues can be successfully addressed. To this end, we first provide a concise and accessible introduction to normalized random measures with independent increments. Then, we explain in detail a particular way of sampling from the posterior using the Ferguson-Klass representation. We develop a thorough comparative analysis for location-scale mixtures that considers a set of alternatives for the mixture kernel and for the nonparametric component. Simulation results indicate that normalized random measure mixtures potentially represent a valid default choice for density estimation problems. As a byproduct of this study an R package to fit these models was produced and is available in the Comprehensive R Archive Network (CRAN).