# Finite mixture models do not reliably learn the number of components

@inproceedings{Cai2021FiniteMM, title={Finite mixture models do not reliably learn the number of components}, author={Diana Cai and Trevor Campbell and Tamara Broderick}, booktitle={ICML}, year={2021} }

Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. A common suggestion is to use a finite mixture model (FMM) with a prior on the number of components. Past work has shown the resulting FMM component-count posterior is consistent; that is, the posterior concentrates on the true generating number of components. But existing results crucially depend on the assumption that the component likelihoods are perfectly specified… Expand

#### References

SHOWING 1-10 OF 86 REFERENCES

On posterior contraction of parameters and interpretability in Bayesian mixture modeling

- Mathematics
- 2019

Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data

- Computer Science, Medicine
- ICML
- 2016

On strong identifiability and convergence rates of parameter estimation in finite mixtures

- Mathematics
- 2016

A simple example of Dirichlet process mixture inconsistency for the number of components

- Computer Science, Mathematics
- NIPS
- 2013