# krasserm/bayesian-machine-learning

Notebooks about Bayesian methods for machine learning

Repository: https://github.com/krasserm/bayesian-machine-learning
Canonical: https://ross.abutalabs.com/products/bayesian-machine-learning
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: machine-learning, bayesian-methods, bayesian-machine-learning, gaussian-processes, bayesian-optimization, variational-autoencoder
Last push: 2026-07-12T05:22:49+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 92, release rhythm 8, longevity 100
- inputs: {"age_days": 3089, "days_push": 52, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1917, forks 474 (observed 2026-08-28T04:05:53.867442+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science
- domain: machine-learning, tutorials, data-science
- platform: python
- tags: jupyter-notebooks, bayesian-methods, gaussian-processes, bayesian-optimization, variational-inference, education

## Member repositories
- krasserm/bayesian-machine-learning (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:53.867442+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:10:09.417607+00:00, confidence not recorded.
  - readme: https://github.com/krasserm/bayesian-machine-learning (fetched 2026-08-28T04:05:53.867442+00:00, sha 19d3b48e9acc)
- Data as of 2026-08-30T08:39:29.467469+00:00.