bayesianbandits
===============

Bayesian multi-armed bandits with conjugate online learning. Sometimes, 
you need to know your decisions are close to optimal at all times.

**Getting started**
   New to bandits? The :doc:`introduction <introduction>` explains when
   you'd use one and how to choose your setup. The :doc:`quickstart`
   walks through a complete example in 5 minutes.

**How-to guides**
   Task-oriented recipes: :doc:`pipelines <howto/pipelines>`,
   :doc:`decay <howto/decay>`, :doc:`reward functions <howto/reward-functions>`,
   :doc:`delayed rewards <howto/delayed-rewards>`,
   :doc:`production deployment <howto/production>`,
   :doc:`sparse features <howto/sparse>`.

**Explanation**
   Why this library makes the choices it does: :doc:`explanation/index`.

**Mathematical reference**
   Update equations, hyperparameter semantics, and departures from
   textbook for each estimator family: :doc:`math/index`.

**API reference**
   Full details on every class and method: :doc:`api`.

.. toctree::
   :maxdepth: 2
   :hidden:

   getting-started
   howto/index
   explanation/index
   math/index
   examples
   api
   changelog