aGrUM 2.3.2
a C++ library for (probabilistic) graphical models
GibbsSampling.h
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48
49
50#ifndef GUM_GIBBS_SAMPLING_H
51#define GUM_GIBBS_SAMPLING_H
52
54
55namespace gum {
56
70
71 template < typename GUM_SCALAR >
72 class GibbsSampling: public SamplingInference< GUM_SCALAR >, public GibbsOperator< GUM_SCALAR > {
73 public:
77 explicit GibbsSampling(const IBayesNet< GUM_SCALAR >* bn);
78
82 ~GibbsSampling() override;
83
89 void setBurnIn(Size b) { this->burn_in_ = b; };
90
95 Size burnIn() const { return this->burn_in_; };
96
97 protected:
99 Instantiation burnIn_() override;
100
102
115 Instantiation draw_(GUM_SCALAR* w, Instantiation prev) override;
116
118
129 };
130
131
132#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
133 extern template class GibbsSampling< double >;
134#endif
135} // namespace gum
136
138#endif
Implementation of Gibbs Sampling for inference in Bayesian networks.
Size burn_in_
Number of iterations before checking stopping criteria.
GibbsOperator(const IBayesNet< GUM_SCALAR > &BN, const NodeProperty< Idx > *hardEv, Size nbr=1, bool atRandom=false)
constructor
~GibbsSampling() override
Destructor.
Instantiation draw_(GUM_SCALAR *w, Instantiation prev) override
draws a sample given previous one according to Gibbs sampling
Instantiation burnIn_() override
draws a defined number of samples without updating the estimators
Instantiation monteCarloSample_()
draws a Monte Carlo sample
void setBurnIn(Size b)
Number of burn in for one iteration.
Size burnIn() const
Returns the number of burn in.
GibbsSampling(const IBayesNet< GUM_SCALAR > *bn)
Default constructor.
Class representing the minimal interface for Bayesian network with no numerical data.
Definition IBayesNet.h:75
Class for assigning/browsing values to tuples of discrete variables.
SamplingInference(const IBayesNet< GUM_SCALAR > *bn)
default constructor
std::size_t Size
In aGrUM, hashed values are unsigned long int.
Definition types.h:74
gum is the global namespace for all aGrUM entities
Definition agrum.h:46
This file contains general methods for simulation-oriented approximate inference.