mbdlyb.formalisms.markovnet.reasoner
====================================

.. py:module:: mbdlyb.formalisms.markovnet.reasoner

.. autoapi-nested-parse::

   Markov Network inference engine and reasoning operations.

   This module provides inference capabilities for Markov networks, including
   exact inference using belief propagation algorithms and information-theoretic
   computations such as entropy and conditional entropy.

   Classes:
       MarkovNetReasoner: The main inference engine for Markov networks.



Classes
-------

.. autoapisummary::

   mbdlyb.formalisms.markovnet.reasoner.MarkovNetReasoner


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

.. py:class:: MarkovNetReasoner(net: mbdlyb.formalisms.markovnet.base.MarkovNet, **kwargs)

   Bases: :py:obj:`mbdlyb.MBDReasoner`


   Inference engine for Markov networks (undirected graphical models).

   Supports exact inference using Shafer-Shenoy message passing algorithm.
   Provides methods for inference, entropy computation, and conditional entropy
   calculations on Markov random fields.

   :ivar _net: The MarkovNet instance being reasoned over.
   :ivar _mn: The underlying pyAgrum MarkovRandomField structure.
   :ivar _ie: The pyAgrum inference engine instance (ShaferShenoyMRFInference).



   .. py:method:: conditional_entropy(y: set[mbdlyb.MBDNode], x: set[mbdlyb.MBDNode], evidence: Optional[mbdlyb.Evidence] = None) -> float

      Compute the conditional entropy H(Y|X).

      Calculates the entropy of variable set y given variable set x,
      optionally conditioned on additional evidence.

      :param y: The set of nodes for which to compute entropy.
      :param x: The conditioning variable set.
      :param evidence: Optional evidence to apply before computation.

      :returns: The conditional entropy H(Y|X) in bits.



   .. py:method:: entropy(y: set[mbdlyb.MBDNode], evidence: Optional[mbdlyb.Evidence] = None) -> float

      Compute the entropy H(Y) of a set of nodes.

      Calculates the Shannon entropy of the joint distribution of a set of
      variables, optionally conditioned on evidence.

      :param y: The set of nodes for which to compute entropy.
      :param evidence: Optional evidence to apply before computation.

      :returns: The entropy H(Y) in bits.



   .. py:method:: infer(*targets: mbdlyb.MBDNode) -> mbdlyb.reasoner.T_POSTERIORS

      Perform inference on target nodes given current evidence.

      Computes the posterior probability distributions for the specified target
      nodes given the current evidence set in the reasoner.

      :param \*targets: One or more MBDNode instances to compute posteriors for.

      :returns: A dictionary mapping each target node to a dictionary of its states
                and their posterior probabilities.



   .. py:method:: init()

      Initialize the inference engine.

      Converts the abstract Markov network to a pyAgrum MarkovRandomField and
      creates the Shafer-Shenoy inference engine instance.