krasserm/bayesian-machine-learning resource

Health v2 · maintenance only

Adoption not part of the score

1917 stars · 474 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks teaching Bayesian machine learning, covering Bayesian linear regression, Gaussian processes, sparse Gaussian processes, Bayesian optimization, and variational autoencoders. Implementations use NumPy, SciPy, scikit-learn, GPy, JAX, and PyMC3.

Use cases

• learn bayesian machine learning
• understand gaussian processes for regression
• tutorial on bayesian optimization for hyperparameter tuning
• learn variational autoencoders
• implement gaussian processes from scratch with numpy
• study bayesian linear regression

When to choose

• you want pedagogical, math-heavy notebooks explaining Bayesian methods with runnable code
• you want to see the same models implemented in multiple libraries (NumPy, scikit-learn, GPy, JAX, PyMC3)

When to avoid

• you need a production-ready Bayesian ML library rather than educational notebooks
• you need a maintained software package with an API instead of example code

Facets

learning-resource · maturity stable

machine-learning data-science machine-learning tutorials data-science python jupyter-notebooks bayesian-methods gaussian-processes bayesian-optimization variational-inference education

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markdown · JSON · MCP: product_card(name="krasserm/bayesian-machine-learning")