public
================

.. py:module:: jaxns.experimental.public

.. rubric:: :code:`jaxns.experimental.public`


.. rubric:: Module Contents

.. py:class:: GlobalOptimisation

   A global optimiser using nested sampling as the core algorithm. Can easily globally optimise complex models, with
   curving degeneracies and multimodal structure. Highly parallelisable. Recommended to use gradient information by
   setting gradient_slice=True.

   Note, the log-likelihood over the model is maximised NOT the posterior. The prior acts as the search space prior,
   by constraining the search space and giving search preference to regions of high prior probability. Thus, the
   prior should encode your prior belief about where you think the global maximum is located.

   :param model: a model to perform global optimisation on over the sample space.
   :param num_search_chains: number of search chains to use.
   :param s: number of slices to use per dimension.
   :param k: number of phantom samples to use.
   :param gradient_slice: if true use gradient information to improve. Default True.
   :param shell_frac: fraction of the shell to discard in parallel.
   :param devices: devices to use for parallel sharded computation. Default all available devices.
   :param verbose: whether to print verbose output. Default False.


   .. py:attribute:: model
      :type:  jaxns.framework.bases.BaseAbstractModel


   .. py:attribute:: num_search_chains
      :type:  Optional[int]
      :value: None



   .. py:attribute:: s
      :type:  Optional[int]
      :value: None



   .. py:attribute:: k
      :type:  Optional[int]
      :value: None



   .. py:attribute:: gradient_slice
      :type:  bool
      :value: True



   .. py:attribute:: shell_frac
      :type:  Optional[float]
      :value: None



   .. py:attribute:: devices
      :type:  Optional[jaxlib.xla_client.Device]
      :value: None



   .. py:attribute:: verbose
      :type:  bool
      :value: False



   .. py:method:: __post_init__()


   .. py:method:: __call__(key, term_cond = None, finetune = False)

      Runs the global optimisation.

      :param key: PRNGKey
      :param term_cond: termination condition
      :param finetune: whether to use gradient-based fine-tune. Default False because not all models have gradients.

      :returns: results of the global optimisation



.. py:data:: DefaultGlobalOptimisation