What are Gaussian Mixture Models and how do they work?

What are Gaussian Mixture Models and how do they work?

Gaussian Mixture Models (GMMs) are probabilistic models that assume data points are generated from a mixture of several Gaussian distributions. They are used for clustering and density estimation.

How do I fit a GMM to my data?

To fit a GMM, you can use the Expectation-Maximization (EM) algorithm. It iteratively estimates the parameters of the Gaussian components until convergence.

What are some applications of GMMs?

GMMs are widely used in image processing, speech recognition, and anomaly detection, among other fields.

Can you explain the difference between GMMs and K-means clustering?

Sure! GMMs provide a probabilistic approach and can model elliptical clusters, while K-means assumes spherical clusters and assigns each point to the nearest cluster.