Bayesian Methods
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Probabilistic approaches that model uncertainty by maintaining distributions over parameters rather than point estimates. Provides principled uncertainty quantification and works well with small datasets.
Key Properties
- Prior and posterior distributions
- Gaussian processes for regression
- Probabilistic programming (PyMC, Stan, NumPyro)
Related
- Naive Bayes (simplest Bayesian classifier)
- Hyperparameter Tuning (Bayesian optimization)