Mixture Model
1σ ellipse
2σ ellipse
Mean
Soft Assignments (Responsibilities)
γₖ(xᵢ) = πₖ N(xᵢ|μₖ,Σₖ) / Σⱼ πⱼ N(xᵢ|μⱼ,Σⱼ)
Posterior probability of component k
Log-Likelihood
Probability Density
Data Points
Click on the mixture canvas to add points
Number of Components (K)
3
Gaussians
EM Algorithm
0
Iteration
Covariance Type
Model Statistics
Points
0
Log-Lik
--
BIC
--
AIC
--
Quick Examples
How it works: GMM models data as a mixture of K Gaussians. The EM algorithm alternates: E-step computes soft assignments (responsibilities), M-step updates means, covariances, and mixing weights. Unlike K-means, GMM provides probabilistic cluster memberships.