In this tutorial, we describe how to include a new utility (specifically, a sampler)
to the surmise's framework. We illustrate this with metropolis_hastings--a
well-known sampler located in the directory \utilitiesmethods.
In surmise, all utilities inherit from the base class :py:class:`surmise.utilities.sampler`. Note that for now, we only have samplers as utilities. Later, we plan to have different classes (such as :py:class:`surmise.utilities.optimizer`) that can be used during calibration.
A sampler takes the function returning the log of the posterior of given theta as an input, and returns a dictionary including a random sample of thetas from the posterior distribution.
:py:func:`sampler` is the only obligatory function for a sampler.
The :py:func:`metropolis_hastings.sampler` is given below for an illustration:
.. automodule:: metropolis_hastings
:members:
:undoc-members:
:show-inheritance:
Once the base class :py:class:`surmise.utilities.sampler` is initialized, :py:func:`surmise.utilities.sampler.draw_samples` method calls the developer's sampler's :py:func:`sampler` function, and places all information into the dictionary, and returns it.
None. This section is under development.