50#ifndef GUM_MONTE_CARLO_INFERENCE_H
51#define GUM_MONTE_CARLO_INFERENCE_H
71 template <
typename GUM_SCALAR >
104#ifndef GUM_NO_EXTERN_TEMPLATE_CLASS
Implementation of Monte Carlo Sampling for inference in Bayesian Networks.
Class representing the minimal interface for Bayesian network with no numerical data.
Class for assigning/browsing values to tuples of discrete variables.
Instantiation draw_(GUM_SCALAR *w, Instantiation prev) override
draws a sample according to classic Monte Carlo sampling
MonteCarloSampling(const IBayesNet< GUM_SCALAR > *bn)
Default constructor.
~MonteCarloSampling() override
Destructor.
Instantiation burnIn_() override
draws a defined number of samples without updating the estimators
SamplingInference(const IBayesNet< GUM_SCALAR > *bn)
default constructor
gum is the global namespace for all aGrUM entities
This file contains general methods for simulation-oriented approximate inference.