96 template < GUM_Numeric GUM_SCALAR >
136#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
algorithm for KL divergence between BNs
KL divergence between BNs – implementation using independent Monte Carlo sampling.
This file contains general scheme for iteratively convergent algorithms.
ApproximationScheme(bool verbosity=false)
BNdistance(const IBayesNet< GUM_SCALAR > &P, const IBayesNet< GUM_SCALAR > &Q)
constructor must give 2 BNs
const IBayesNet< GUM_SCALAR > & q_
const IBayesNet< GUM_SCALAR > & p_
Class representing the minimal interface for Bayesian network with no numerical data.
Class for assigning/browsing values to tuples of discrete variables.
MCBNDistance computes the KL divergence betweens 2 BNs using an approximation pattern: independent (f...
Instantiation drawSample_(const Sequence< NodeId > &topoOrder) const
draws an instantiation of p_'s variables, independently, following topoOrder (no evidence to handle i...
MCBNDistance(const IBayesNet< GUM_SCALAR > &P, const IBayesNet< GUM_SCALAR > &Q)
constructor must give 2 BNs
~MCBNDistance() override
destructor
gum is the global namespace for all aGrUM entities