54 lines
1.6 KiB
C++
54 lines
1.6 KiB
C++
/**
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* @file inference.h
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* @brief Contains *generic* inference algorithms that convert between templated
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* graphical models, i.e., factor graphs, Bayes nets, and Bayes trees
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* @author Frank Dellaert
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*/
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#pragma once
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#include "FactorGraph.h"
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#include "BayesNet.h"
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#include "Key.h"
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namespace gtsam {
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class Ordering;
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// ELIMINATE: FACTOR GRAPH -> BAYES NET
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/**
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* Eliminate a single node yielding a Conditional
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* Eliminates the factors from the factor graph through findAndRemoveFactors
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* and adds a new factor on the separator to the factor graph
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*/
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template<class Factor, class Conditional>
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boost::shared_ptr<Conditional>
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eliminateOne(FactorGraph<Factor>& factorGraph, const Symbol& key);
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/**
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* eliminate factor graph using the given (not necessarily complete)
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* ordering, yielding a chordal Bayes net and (partially eliminated) FG
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*/
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template<class Factor, class Conditional>
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BayesNet<Conditional> eliminate(FactorGraph<Factor>& factorGraph, const Ordering& ordering);
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// FACTOR/MARGINALIZE: BAYES NET -> FACTOR GRAPH
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/**
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* Factor P(X) as P(not keys|keys) P(keys)
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* @return P(not keys|keys) as an incomplete BayesNet, and P(keys) as a factor graph
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*/
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template<class Factor, class Conditional>
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std::pair< BayesNet<Conditional>, FactorGraph<Factor> >
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factor(const BayesNet<Conditional>& bn, const Ordering& keys);
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/**
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* integrate out all except ordering, might be inefficient as the ordering
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* will simply be the current ordering with the keys put in the back
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*/
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template<class Factor, class Conditional>
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FactorGraph<Factor> marginalize(const BayesNet<Conditional>& bn, const Ordering& keys);
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} /// namespace gtsam
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