# Truncation-free Hybrid Inference for DPMM

## TL;DR

This paper introduces a truncation-free hybrid inference method for Dirichlet process mixture models that combines MCMC and variational approaches, improving efficiency and flexibility over traditional methods.

## Contribution

It presents a novel hybrid inference technique that avoids truncation, enhancing computational efficiency and model adaptability in DPMMs.

## Key findings

- Hybrid inference outperforms traditional variational methods.
- Method is easy to implement and scales well.
- Empirical results show improved model complexity management.

## Abstract

Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian non-parametrics. While these models free from choosing the number of components a-priori, computationally attractive variational inference often reintroduces the need to do so, via a truncation on the variational distribution. In this paper we present a truncation-free hybrid inference for DPMM, combining the advantages of sampling-based MCMC and variational methods. The proposed hybridization enables more efficient variational updates, while increasing model complexity only if needed. We evaluate the properties of the hybrid updates and their empirical performance in single- as well as mixed-membership models. Our method is easy to implement and performs favorably compared to existing schemas.