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Bayesian Machine Learning

Bayesian Machine Learning is a public repository of Jupyter notebooks that explain and implement Bayesian machine-learning methods, including regression, Gaussian processes, optimization, neural networks and latent-variable models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bayesian Machine Learning — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to understand a Bayesian method through an explanatory notebook and its example implementation, or identify a starting point for an experiment.

Who it is for

  • Learners studying Bayesian machine-learning concepts through worked implementations.
  • Practitioners looking for notebook examples to adapt for experiments involving probabilistic regression, optimization or latent-variable models.

Documented uses

  • Study Bayesian linear regression and compare NumPy/scikit-learn and PyMC3 implementations.
  • Explore Gaussian processes for regression, classification and sparse modeling.
  • Follow a Bayesian optimization example using hyperparameter tuning as an application.
  • Examine variational inference, Bayesian neural networks, uncertainty estimation and variational autoencoders.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. Selected notebooks link to Google Colab; the README also links notebooks through nbviewer. Dependencies are specified in requirements files in subdirectories.

  • Examples have topic-specific dependencies; consult the relevant subdirectory requirements file.
  • Implementation libraries vary by notebook, including NumPy, SciPy, scikit-learn, GPy, JAX, Keras and TensorFlow.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Explore a Bayesian linear regression notebook

Use the repository’s notebook as an introduction to Bayesian linear regression.

Before you start

  • Access to the repository’s Bayesian regression notebook.

Steps

  1. Open the Bayesian linear regression notebook linked in the repository README.

  2. Use it to explore the introduction and its implementation with NumPy and scikit-learn.

  3. For the alternative implementation, open the linked PyMC3 notebook.

Expected outcome

A notebook-based introduction to Bayesian linear regression, with NumPy/scikit-learn and PyMC3 implementations available.

Guide limitations

  • The README describes the notebook content but does not provide instructions for installing dependencies or executing the notebooks.
  • Dependencies are specified in requirements.txt files in subdirectories.
Documentation 1 ↗
Source-based practical guide

Explore a Gaussian processes notebook in Colab

Study the repository’s introductory Gaussian processes notebook for regression.

Before you start

  • Access to the linked Google Colab notebook.

Steps

  1. Open the Gaussian processes notebook using its Colab link in the repository README.

  2. Use the notebook as an introduction to Gaussian process regression and its NumPy/SciPy, scikit-learn, and GPy implementations.

Expected outcome

A notebook-based introduction to Gaussian process regression and listed implementation options.

Guide limitations

  • Dependencies are specified in requirements.txt files in subdirectories.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
11 October 2026
Original website title
krasserm/bayesian-machine-learning · Official homepage title · retained in its source language
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