Bayesian Machine Learning

Bayesian ML Methods

Bayesian approach quantifies uncertainty.

Bayesian Regression

Place priors on coefficients. Posterior: P(θ|D) ∝ P(D|θ)P(θ). MAP estimates.

Full posterior gives uncertainty.

Gaussian Processes

Non-parametric Bayesian. Prior over functions. Posterior predicts with uncertainty.

gp = GaussianProcessRegressor(). fit(X, y). predict(X_new, return_std=True).

Variational Inference

Approximate posterior for complex models. Enables scalable Bayesian methods.

Key Takeaways

1. Bayesian methods provide uncertainty estimates
2. GP for non-parametric regression
3. Variational inference scales Bayesian methods