Markov Chain Monte Carlo sampler. More...

#include <mcmc_sampler.hpp>

Collaboration diagram for bayesopt::MCMCSampler:

Public Member Functions
	MCMCSampler (RBOptimizable *rbo, size_t dim, randEngine &eng)
	Constructor (Note: default constructor is private) More...

void	setAlgorithm (McmcAlgorithms newAlg)
	Sets the sampling algorithm (slice, MH, etc.)

void	setNParticles (size_t nParticles)
	Sets the number of particles that are stored.

void	setNBurnOut (size_t nParticles)
	Usually, the initial samples of any MCMC method are biased and they are discarded. More...

void	run (vectord &Xnext)
	Compute the set of particles according to the target PDF. More...

vectord	getParticle (size_t i)

void	printParticles ()

Private Member Functions
void	randomJump (vectord &x)

void	burnOut (vectord &x)

void	sliceSample (vectord &x)

	MCMCSampler (MCMCSampler &copy)

Private Attributes
boost::scoped_ptr< RBOptimizableWrapper >	obj

McmcAlgorithms	mAlg

size_t	mDims

size_t	nBurnOut

size_t	nSamples

bool	mStepOut

vectord	mSigma

vecOfvec	mParticles

randEngine &	mtRandom

Detailed Description

Markov Chain Monte Carlo sampler.

It generates a set of particles that are distributed according to an arbitrary pdf. IMPORTANT: As it should be a replacement for the optimization (ML or MAP) estimation, it also assumes a NEGATIVE LOG PDF.

See also: NLOPT_Optimization

Definition at line 50 of file mcmc_sampler.hpp.

Constructor & Destructor Documentation

◆ MCMCSampler()

bayesopt::MCMCSampler::MCMCSampler	(	RBOptimizable *	rbo,
		size_t	dim,
		randEngine &	eng
	)

Constructor (Note: default constructor is private)

Parameters

rbo	point to RBOptimizable type of object with the PDF to sample from. IMPORTANT: We assume that the evaluation of rbo is the NEGATIVE LOG PDF.
dim	number of input dimensions
eng	random number generation engine (boost)

Definition at line 28 of file mcmc_sampler.cpp.

References bayesopt::SLICE_MCMC.

Member Function Documentation

◆ run()

void bayesopt::MCMCSampler::run ( vectord & Xnext )

Compute the set of particles according to the target PDF.

Parameters

Xnext input: initial point of the Markov Chain, output: last point of the Markov Chain

Definition at line 131 of file mcmc_sampler.cpp.

◆ setNBurnOut()

void bayesopt::MCMCSampler::setNBurnOut ( size_t nParticles )

inline

Usually, the initial samples of any MCMC method are biased and they are discarded.

This phase is called the burnout. This method sets the number of particles to be discarded

Definition at line 115 of file mcmc_sampler.hpp.

The documentation for this class was generated from the following files:

Public Member Functions