Boax: A Bayesian Optimization library for JAX.
===============================================

.. note::

   Boax is currently in early alpha and under active development!

Boax is a composable library of core components for Bayesian Optimization
that is **designed for flexibility**.

It comes with high-level interfaces for:

* **Experiments** (`boax.experiments`):

  * Bayesian Optimization Setups
  * Bandit Optimization Setups
  * Search Spaces

And with low-level interfaces for:

* **Constructing acquisition functions** (`boax.acquisition`):

  * Acquisition Functions
  * Surrogate Models

* **Constructing policy functions** (`boax.policies`):
  
  * Policy Functions
  * Believes

* **Core capabilities** (`boax.core`):

  * Common Distributions
  * Gaussian Process Models
  * Objective Functions
  * Quasi-Newton Optimizers
  * Monte-Carlo Samplers

----

Installation
^^^^^^^^^^^^

The latest release of Boax can be installed from
`PyPI <https://pypi.org/project/boax/>`_ using::

   pip install boax

You may also install directly from GitHub, using the following command. This
can be used to obtain the most recent version of Boax::

   pip install git+git://github.com/Lando-L/boax.git


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

   guides/Getting_Started
   guides/index
   api_reference/index