sequince.steps.mutation.blocked
===============================

.. py:module:: sequince.steps.mutation.blocked

.. autoapi-nested-parse::

   Blocked Metropolis-Hastings mutation kernels and steps.



Classes
-------

.. autoapisummary::

   sequince.steps.mutation.blocked.MetropolisBlock
   sequince.steps.mutation.blocked.BlockedMetropolisHastingsMutation


Module Contents
---------------

.. py:class:: MetropolisBlock

   Configuration for one Metropolis-within-Gibbs parameter block.


   .. py:attribute:: name
      :type:  str


   .. py:attribute:: kernel
      :type:  sequince.steps.mutation.kernels.MutationKernel | sequince.steps.mutation.metropolis.MutationKernelFactory


   .. py:attribute:: n_steps
      :type:  int
      :value: 1



.. py:class:: BlockedMetropolisHastingsMutation(log_prob_fn, blocks, *, transform = None, transform_refit = 'always', store_chains = False, metrics = ())

   Bases: :py:obj:`sequince.steps.base.SMCStep`


   Apply sequential Metropolis-Hastings updates to parameter blocks.

   Each block targets the complete log probability while its kernel changes
   only a subset of coordinates. The composition is therefore a valid
   Metropolis-within-Gibbs rejuvenation step.

   Coordinate indices refer to the space in which the kernels operate. When a
   transform is supplied, it should preserve the requested block structure
   (for example, a coordinate-wise bounds transform rather than a dense
   whitening transform).

   .. rubric:: Example

   >>> from sequince import steps, transforms
   >>> from sequince.steps.mutation import kernels
   >>> blocks = [
   ...     steps.mutation.MetropolisBlock(
   ...         name="block1",
   ...         kernel=kernels.AdaptiveMaskedGaussianRandomWalk(
   ...             indices=[0, 1],
   ...             scale=0.1,
   ...         ),
   ...         n_steps=2,
   ...     ),
   ...     steps.mutation.MetropolisBlock(
   ...         name="block2",
   ...         kernel=kernels.AdaptiveMaskedGaussianRandomWalk(
   ...             indices=[2, 3],
   ...             scale=0.2,
   ...         ),
   ...         n_steps=3,
   ...     ),
   ... ]
   ... step = steps.BlockedMetropolisHastingsMutation(
   ...     log_prob_fn=posterior.log_prob,
   ...     blocks=blocks,
   ... )


   .. py:attribute:: metrics


   .. py:method:: apply(state, context, rng)