bayesian-mixture-models
Bayesian Mixture Models — Soft Clustering with Uncertainty
For clustering where you need probabilistic assignments and uncertainty on cluster parameters, use Bayesian Gaussian mixture models with PyMC. Unlike k-means (hard assignments, no uncertainty) or sklearn GMM (soft assignments, no parameter uncertainty), the Bayesian approach gives you posteriors on everything: means, variances, weights, and per-point assignments.
When to use this skill
- You need soft cluster assignments (each point has a probability of belonging to each cluster)
- You want uncertainty on cluster locations, shapes, and weights
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