multi_slice_sampler
=============================

.. py:module:: jaxns.samplers.multi_slice_sampler

.. rubric:: :code:`jaxns.samplers.multi_slice_sampler`


.. rubric:: Module Contents

.. py:class:: MultiDimSliceSampler

   Bases: :py:obj:`jaxns.samplers.bases.BaseAbstractMarkovSampler`\ [\ :py:obj:`jaxns.nested_samplers.common.types.SampleCollection`\ ]


   Multi-dimensional slice sampler, with exponential shrinkage. Produces correlated (non-i.i.d.) samples.

   Notes: Not very efficient.

   :param model: AbstractModel
   :param num_slices: number of slices between acceptance, in units of 1, unlike other software which does it in
                      units of prior dimension.
   :param num_phantom_save: number of phantom samples to save. Phantom samples are samples that meeting the constraint
                            but are not accepted. They can be used for numerous things, e.g. to estimate the evidence uncertainty.
   :param num_restrict_dims: size of subspace to slice along. Setting to 1 would be like UniDimSliceSampler,
                             but far less efficient.


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


   .. py:attribute:: num_slices
      :type:  int


   .. py:attribute:: num_phantom_save
      :type:  int


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



   .. py:method:: __post_init__()

      



   .. py:method:: num_phantom()


   .. py:method:: get_seed_point(key, sampler_state, log_L_constraint)


   .. py:method:: get_sample_from_seed(key, seed_point, log_L_constraint, sampler_state)