bayesianbandits

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A Pythonic microframework for multi-armed bandit problems

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- - build.yml
  - ci.yml
  - matrix_test.yml
- conftest.py
- test_bench_estimators.py
- test_bench_forgetting.py
- test_bench_ids_select.py
- test_bench_marginal_sampling.py
- test_bench_reward_space_sampling.py
- test_bench_support_covariance.py
- - style.css
- - decision-theory.rst
  - index.rst
  - worldview.rst
- - bayesianbandits.Agent.rst
  - bayesianbandits.AgentPipeline.rst
  - bayesianbandits.Arm.rst
  - bayesianbandits.ArmColumnFeaturizer.rst
  - bayesianbandits.ArmFeaturizer.rst
  - bayesianbandits.BayesianGLM.rst
  - bayesianbandits.ContextualAgent.rst
  - bayesianbandits.DirichletClassifier.rst
  - bayesianbandits.DrawKind.rst
  - bayesianbandits.EmpiricalBayesDirichletClassifier.rst
  - bayesianbandits.EmpiricalBayesGammaRegressor.rst
  - bayesianbandits.EmpiricalBayesGLM.rst
  - bayesianbandits.EmpiricalBayesNormalRegressor.rst
  - bayesianbandits.EpsilonGreedy.rst
  - bayesianbandits.EXP3A.rst
  - bayesianbandits.FunctionArmFeaturizer.rst
  - bayesianbandits.GammaRegressor.rst
  - bayesianbandits.InformationDirectedSampling.rst
  - bayesianbandits.LaplaceApproximator.rst
  - bayesianbandits.LearnerPipeline.rst
  - bayesianbandits.LipschitzContextualAgent.rst
  - bayesianbandits.NormalInverseGammaRegressor.rst
  - bayesianbandits.NormalRegressor.rst
  - bayesianbandits.RVGAApproximator.rst
  - bayesianbandits.ThompsonSampling.rst
  - bayesianbandits.UpperConfidenceBound.rst
- - decay.rst
  - delayed-rewards.rst
  - index.rst
  - pipelines.rst
  - production.rst
  - reward-functions.rst
  - sparse.rst
- - dirichlet-eb.rst
  - empirical-bayes.rst
  - forgetting.rst
  - gamma-eb.rst
  - glm-eb.rst
  - glm.rst
  - index.rst
  - intercept-only.rst
  - normal-inverse-gamma.rst
  - normal.rst
  - policies.rst
- - adversarial.ipynb
  - delayed-reward.ipynb
  - empirical-bayes.ipynb
  - hybrid-bandits.ipynb
  - linear-bandits.ipynb
  - offline-learning.ipynb
  - rvga-glm.ipynb
- api.rst
- changelog.rst
- conf.py
- examples.rst
- getting-started.rst
- index.rst
- introduction.rst
- make.bat
- Makefile
- quickstart.rst
- - __init__.py
  - _arm.py
  - _arm_featurizer.py
  - _blas_helpers.py
  - _draw_kind.py
  - _eb_estimators.py
  - _empirical_bayes.py
  - _estimators.py
  - _forgetting.py
  - _gaussian.py
  - _memory.py
  - _np_utils.py
  - _sparse_bayesian_linear_regression.py
  - _support_covariance.py
  - _takahashi.pyx
  - api.py
- - matrix_0
  - matrix_1
  - matrix_10
  - matrix_2
  - matrix_3
  - matrix_4
  - matrix_5
  - matrix_6
  - matrix_7
  - matrix_8
  - matrix_9
- __init__.py
- _helpers.py
- conftest.py
- test_agent_pipeline.py
- test_api.py
- test_arm.py
- test_arm_column_featurizer.py
- test_arm_featurizer.py
- test_arm_learner_independence.py
- test_batch_reward_examples.py
- test_batch_reward_function.py
- test_bayesian_glm.py
- test_blas_helpers.py
- test_context_joint_wiring.py
- test_draw_kind.py
- test_eb_estimators.py
- test_eb_glm.py
- test_empirical_bayes.py
- test_empirical_bayes_glm.py
- test_estimators.py
- test_exp3a.py
- test_forgetting.py
- test_gaussian_posterior_laplace.py
- test_howto_decay.py
- test_howto_delayed_rewards.py
- test_howto_pipelines.py
- test_howto_production.py
- test_howto_reward_functions.py
- test_howto_sparse.py
- test_information_directed_sampling.py
- test_joint_reduction.py
- test_learner_pipeline.py
- test_link_functions.py
- test_lipschitz_contextual_agent.py
- test_marginal_sampling.py
- test_mathematical_correctness.py
- test_memory.py
- test_normal_estimators.py
- test_partitioned_factor.py
- test_prior_floor.py
- test_quickstart.py
- test_reward_space_sampling.py
- test_rng_reseed.py
- test_rvga_approximator.py
- test_sample_layout.py
- test_solver_detection.py
- test_sparse_bayesian_linear_regression.py
- test_support_covariance.py
.gitignore
.python-version
.readthedocs.yaml
CLAUDE.md
LICENSE
pyproject.toml
README.md
setup.py
uv.lock

# Installation Guide

1. Get the code

git clone https://github.com/bayesianbandits/bayesianbandits

Downloads the entire project code from GitHub to your computer.

cd bayesianbandits

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install -U bayesianbandits

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -U bayesianbandits

Installs the package published on PyPI directly — no need to clone the source.

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.