Miic.h File Reference

Copyright (c) 2005-2021 by Pierre-Henri WUILLEMIN() & Christophe GONZALES() info_at_agrum_dot_org. More...

#include <string>
#include <vector>
#include <agrum/BN/BayesNet.h>
#include <agrum/config.h>
#include <agrum/tools/core/approximations/IApproximationSchemeConfiguration.h>
#include <agrum/tools/core/approximations/approximationScheme.h>
#include <agrum/tools/core/heap.h>
#include <agrum/tools/graphs/DAG.h>
#include <agrum/tools/graphs/mixedGraph.h>
#include <agrum/tools/stattests/correctedMutualInformation.h>

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Classes
class	gum::learning::GreaterPairOn2nd
class	gum::learning::GreaterAbsPairOn2nd
class	gum::learning::GreaterTupleOnLast
class	gum::learning::Miic
	The miic learning algorithm. More...

Namespaces
	gum
	Copyright (c) 2005-2021 by Pierre-Henri WUILLEMIN() & Christophe GONZALES() info_at_agrum_dot_org.
	gum::learning

Typedefs
using	gum::learning::CondThreePoints = std::tuple< NodeId, NodeId, NodeId, std::vector< NodeId > >
using	gum::learning::CondRanking = std::pair< CondThreePoints *, double >
using	gum::learning::ThreePoints = std::tuple< NodeId, NodeId, NodeId >
using	gum::learning::Ranking = std::pair< ThreePoints *, double >
using	gum::learning::ProbabilisticRanking = std::tuple< ThreePoints *, double, double, double >

Detailed Description

Copyright (c) 2005-2021 by Pierre-Henri WUILLEMIN() & Christophe GONZALES() info_at_agrum_dot_org.

This library is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with this library. If not, see http://www.gnu.org/licenses/.

The 3off2 algorithm

The ThreeOffTwo class implements the 3off2 algorithm as proposed by Affeldt and al. in https://doi.org/10.1186/s12859-015-0856-x. It starts by eliminating edges that correspond to independent variables to build the skeleton of the graph, and then directs the remaining edges to get an essential graph. Latent variables can be detected using bi-directed arcs.

The variant MIIC is also implemented based on https://doi.org/10.1371/journal.pcbi.1005662. Only the orientation phase differs from 3off2, with a diffferent ranking method and different propagation rules.

Author

Quentin FALCAND and Pierre-Henri WUILLEMIN() and Maria Virginia RUIZ CUEVAS

Definition in file Miic.h.