# CRP clustering

## Summary

The predictive rule for Chinese Restaurant Process (CRP) can be used to define an "infinite-capacity" prior distribution on the clusters in a clustering model. The most common clustering model that uses the CRP is an unbounded analogue to a Gaussian mixture model, where the "table assignments" from the CRP determine the mixture component assignments for each data point.

## Context

This concept has the prerequisites:

- mixture of Gaussians models (Mixture of Gaussians is the canonical example of CRP clustering.)
- Bayesian parameter estimation (CRP clustering is a kind of Bayesian model.)
- Chinese restaurant process
- collapsed Gibbs sampling (Collapsed Gibbs sampling is a simple way of performing inference in a CRP clustering model.)

## Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)

## Supplemental resources (the following are optional, but you may find them useful)

## -Free-

→ Bayesian nonparametric lecture notes (COS 597C)

## See also

- The Indian buffet process linear Gaussian model is another Bayesian nonparametric model, but which uses latent binary representations in place of clusters.