aGrUM  0.20.3
a C++ library for (probabilistic) graphical models
unconstrainedEliminationSequenceStrategy.h File Reference

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

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Classes

class  gum::UnconstrainedEliminationSequenceStrategy
 The base class for all elimination sequence algorithms that require only the graph to be triangulated and the nodes' domain sizes to produce the node elimination ordering. More...
 

Namespaces

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

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/. Base Class for all elimination sequence algorithms that require only the graph to be triangulated and the nodes' domain sizes to produce the node elimination ordering.

In many cases, the graph to be triangulated and the nodes domain sizes are the only information needed to compute an elimination ordering. For instance, this is only what is needed for computing junction trees for Bayesian networks or GAI networks. For such cases, the elimination sequence algorithms derived from UnconstrainedEliminationSequenceStrategy are appropriate. There are however other cases in which additional informations must be passed to the elimination sequence algorithm to produce a correct elimination ordering. For instance, computing a strong junction tree for an influence diagram requires the knowledge of a partial ordering on the nodes to be eliminated. For such cases, classes derived from UnconstrainedEliminationSequenceStrategy are not appropriate and those should be derived from other elimination sequence factories, for from instance PartialOrderedEliminationSequenceStrategy.

Author
Christophe GONZALES() and Pierre-Henri WUILLEMIN()

Definition in file unconstrainedEliminationSequenceStrategy.h.