# mixture of Gaussians models

(50 minutes to learn)

## Summary

Mixture of Gaussians is a probabilistic model commonly used for clustering: partitioning a set of data points into a set of clusters, where data points within a cluster are similar to one another.

## Context

This concept has the prerequisites:

## Core resources (read/watch one of the following)

## -Paid-

→ Pattern Recognition and Machine Learning

A textbook for a graduate machine learning course, with a focus on Bayesian methods.

Location:
Section 9.2, up to 9.2.1, pages 430-432

→ Machine Learning: a Probabilistic Perspective

A very comprehensive graudate-level machine learning textbook.

Location:
Section 11.2, pages 337-342

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

## -Free-

→ Bayesian Reasoning and Machine Learning

A textbook for a graudate machine learning course.

Additional dependencies:

- Bayesian networks

## See also

- K-means is a simpler clustering model which is faster to fit and often used as an initialization.
- When there isn't enough data to fit a general mixture of Gaussians, here are some alternative models:
- Bayesian mixture of Gaussians
- mixture of factor analyzers
- co-clustering
- Bayesian clustered tensor factorization