aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
dSeparationAlgorithm.h
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48
49#ifndef GUM_D_SEPARATION_ALGORITHM_H
50#define GUM_D_SEPARATION_ALGORITHM_H
51
52
53#include <agrum/agrum.h>
54
55#include <agrum/BN/IBayesNet.h>
56
57namespace gum {
58
64 public:
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103
110 void requisiteNodes(const DAG& dag,
111 const NodeSet& query,
112 const NodeSet& hardEvidence,
113 const NodeSet& softEvidence,
114 NodeSet& requisite) const;
115
118 template < typename GUM_SCALAR, class TABLE >
120 const NodeSet& query,
121 const NodeSet& hardEvidence,
122 const NodeSet& softEvidence,
123 Set< const TABLE* >& tensors);
124
126 };
127
128
129} /* namespace gum */
130
131
132#ifndef GUM_NO_INLINE
134#endif // GUM_NO_INLINE
135
137
138
139#endif /* GUM_D_SEPARATION_ALGORITHM_H */
Class representing the minimal interface for Bayesian network with no numerical data.
Base class for dag.
Definition DAG.h:121
Class representing the minimal interface for Bayesian network with no numerical data.
Definition IBayesNet.h:75
Representation of a set.
Definition set.h:131
void requisiteNodes(const DAG &dag, const NodeSet &query, const NodeSet &hardEvidence, const NodeSet &softEvidence, NodeSet &requisite) const
Fill the 'requisite' nodeset with the requisite nodes in dag given a query and evidence.
dSeparationAlgorithm()
default constructor
dSeparationAlgorithm & operator=(const dSeparationAlgorithm &from)
copy operator
void relevantTensors(const IBayesNet< GUM_SCALAR > &bn, const NodeSet &query, const NodeSet &hardEvidence, const NodeSet &softEvidence, Set< const TABLE * > &tensors)
update a set of tensors, keeping only those d-connected with query variables given evidence
d-separation analysis (as described in Koller & Friedman 2009)
d-separation analysis (as described in Koller & Friedman 2009)
Set< NodeId > NodeSet
Some typdefs and define for shortcuts ...
gum is the global namespace for all aGrUM entities
Definition agrum.h:46