common
================

.. py:module:: jaxns.nested_samplers.common

.. rubric:: :code:`jaxns.nested_samplers.common`


.. rubric:: Submodules

.. toctree::
   :titlesonly:
   :maxdepth: 1

   initialisation/index.rst
   termination/index.rst
   types/index.rst
   uniform_sample/index.rst


.. rubric:: Package Contents

.. py:class:: TerminationCondition

   Bases: :py:obj:`NamedTuple`


   Contains the termination conditions for the nested sampling run.

   :param ess: The effective sample size, if the ESS (Kish's estimate) is greater than this the run will terminate.
   :param evidence_uncert: The uncertainty in the evidence, if the uncertainty is less than this the run will terminate.
   :param live_evidence_frac: Depreceated use dlogZ.
   :param dlogZ: Terminate if log(Z_current + Z_remaining) - log(Z_current) < dlogZ. Default log(1 + 1e-2)
   :param max_samples: Terminate if the number of samples exceeds this.
   :param max_num_likelihood_evaluations: Terminate if the number of likelihood evaluations exceeds this.
   :param log_L_contour: Terminate if this log(L) contour is reached. A contour is reached if any dead point
                         has log(L) > log_L_contour. Uncollected live points are not considered.
   :param efficiency_threshold: Terminate if the efficiency (num_samples / num_likelihood_evaluations) is less than this,
                                for the last shrinkage iteration.
   :param rtol: finish when the relative value 2*|log_L_max - log_L_min|/|log_L_max + log_L_min| < rol
   :param atol: finish when the absolute |log_L_max - log_L_min| < atol


   .. py:attribute:: ess
      :type:  Optional[Union[jaxns.internals.types.FloatArray, jaxns.internals.types.IntArray]]
      :value: None



   .. py:attribute:: evidence_uncert
      :type:  Optional[jaxns.internals.types.FloatArray]
      :value: None



   .. py:attribute:: live_evidence_frac
      :type:  Optional[jaxns.internals.types.FloatArray]
      :value: None



   .. py:attribute:: dlogZ
      :type:  Optional[jaxns.internals.types.FloatArray]
      :value: None



   .. py:attribute:: max_samples
      :type:  Optional[Union[jaxns.internals.types.FloatArray, jaxns.internals.types.IntArray]]
      :value: None



   .. py:attribute:: max_num_likelihood_evaluations
      :type:  Optional[Union[jaxns.internals.types.FloatArray, jaxns.internals.types.IntArray]]
      :value: None



   .. py:attribute:: log_L_contour
      :type:  Optional[jaxns.internals.types.FloatArray]
      :value: None



   .. py:attribute:: efficiency_threshold
      :type:  Optional[jaxns.internals.types.FloatArray]
      :value: None



   .. py:attribute:: rtol
      :type:  Optional[jaxns.internals.types.FloatArray]
      :value: None



   .. py:attribute:: atol
      :type:  Optional[jaxns.internals.types.FloatArray]
      :value: None



   .. py:attribute:: peak_XL_frac
      :type:  Optional[jaxns.internals.types.FloatArray]
      :value: None



   .. py:method:: __and__(other)


   .. py:method:: __or__(other)


.. py:class:: NestedSamplerResults

   Bases: :py:obj:`NamedTuple`


   Results of the nested sampling run.


   .. py:attribute:: log_Z_mean
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: log_Z_uncert
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: ESS
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: H_mean
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: samples
      :type:  jaxns.internals.types.XType


   .. py:attribute:: parametrised_samples
      :type:  jaxns.internals.types.XType


   .. py:attribute:: U_samples
      :type:  jaxns.internals.types.UType


   .. py:attribute:: log_L_samples
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: log_dp_mean
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: log_X_mean
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: log_posterior_density
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: num_live_points_per_sample
      :type:  jaxns.internals.types.IntArray


   .. py:attribute:: num_likelihood_evaluations_per_sample
      :type:  jaxns.internals.types.IntArray


   .. py:attribute:: total_num_samples
      :type:  jaxns.internals.types.IntArray


   .. py:attribute:: total_phantom_samples
      :type:  jaxns.internals.types.IntArray


   .. py:attribute:: total_num_likelihood_evaluations
      :type:  jaxns.internals.types.IntArray


   .. py:attribute:: log_efficiency
      :type:  jaxns.internals.types.FloatArray


   .. py:attribute:: termination_reason
      :type:  jaxns.internals.types.IntArray


.. py:class:: NestedSamplerState

   Bases: :py:obj:`NamedTuple`


   .. py:attribute:: key
      :type:  jaxns.internals.types.PRNGKey


   .. py:attribute:: next_sample_idx
      :type:  jaxns.internals.types.IntArray


   .. py:attribute:: num_samples
      :type:  jaxns.internals.types.IntArray


   .. py:attribute:: sample_collection
      :type:  StaticStandardSampleCollection