27 #ifndef GUM_D_SEPARATION_H 28 #define GUM_D_SEPARATION_H 96 template <
typename GUM_SCALAR,
template <
typename >
class TABLE >
101 Set<
const TABLE< GUM_SCALAR >* >& potentials);
110 #ifndef GUM_NO_INLINE 112 #endif // GUM_NO_INLINE d-separation analysis (as described in Koller & Friedman 2009)
the d-separation algorithm as described in Koller & Friedman (2009)
void relevantPotentials(const IBayesNet< GUM_SCALAR > &bn, const NodeSet &query, const NodeSet &hardEvidence, const NodeSet &softEvidence, Set< const TABLE< GUM_SCALAR > * > &potentials)
update a set of potentials, keeping only those d-connected with query variables given evidence ...
Class representing Bayesian networks.
Class representing the minimal interface for Bayesian Network.
gum is the global namespace for all aGrUM entities
dSeparation & operator=(const dSeparation &from)
copy operator
void requisiteNodes(const DAG &dag, const NodeSet &query, const NodeSet &hardEvidence, const NodeSet &softEvidence, NodeSet &requisite)
Fill the 'requisite' nodeset with the requisite nodes in dag given a query and evidence.
d-separation analysis (as described in Koller & Friedman 2009)
dSeparation()
default constructor