53#ifndef GUM_GIBBS_KL2_H
54#define GUM_GIBBS_KL2_H
95 template <
typename GUM_SCALAR >
149#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
algorithm for KL divergence between BNs
KL divergence between BNs – implementation using Gibbs 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_
GibbsKL computes the KL divergence betweens 2 BNs using an approximation pattern: GIBBS sampling.
Size burnIn() const
Returns the number of burn in.
void setBurnIn(Size b)
Number of burn in for one iteration.
GibbsBNdistance(const IBayesNet< GUM_SCALAR > &P, const IBayesNet< GUM_SCALAR > &Q)
constructor must give 2 BNs
~GibbsBNdistance()
destructor
GibbsOperator(const IBayesNet< GUM_SCALAR > &BN, const NodeProperty< Idx > *hardEv, Size nbr=1, bool atRandom=false)
constructor
Class representing the minimal interface for Bayesian network with no numerical data.
This file contains Gibbs sampling (for BNs) class definitions.
std::size_t Size
In aGrUM, hashed values are unsigned long int.
gum is the global namespace for all aGrUM entities