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Base Classes for MCMC Methods
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This module provides abstract base classes for our SGMCMC sampling methods. 
All subclasses inheriting from any of these base classes automatically conform 
to the 
`iterator protocol <https://docs.python.org/3/library/stdtypes.html#iterator-types>`_.

This means that extracting the next sample with 
corresponding costs from *any of our samplers* is as simple as:

.. code-block:: python

   sample, cost = next(sampler)


.. module:: mcmc_base_classes

.. autoclass:: MCMCSampler
    :members:
    :special-members:
    :private-members:

For some applications (e.g. `Bayesian Optimization <https://en.wikipedia.org/wiki/Bayesian_optimization>`_), it is important that 
samplers come with as few design choices as possible. 
To reduce the number of such design choices, a recent contribution in the 
literature proposes an on-line *burn-in* procedure. 




.. XXX: Give reference to bohamiann paper and explain burn-in some more

.. autoclass:: BurnInMCMCSampler
    :members:
    :show-inheritance:
    :special-members:
    :private-members: