Collaboration diagram for DREAM: DiffeRential Evolution Adaptive Metropolis:

Modules
	TasmanianDREAM State

	DREAM Sampling Templates

	Enumerated types

	Miscellaneous utility templates, borrowed from TasGrid.

	Probability distributions, analytic formulas and sampling algorithms

	Likelihood definitions

Files
file	TasmanianDREAM.hpp
	DiffeRential Evolution Adaptive Metropolis methods.

file	tsgDreamState.hpp
	The container class holding the DREAM history.

file	tsgDreamSample.hpp
	Core sampling templates.

file	tsgDreamEnumerates.hpp
	The enumerated types used in the DREAM module.

file	tsgDreamCoreRandom.hpp
	Core random sampling methods.

file	tsgDreamCorePDF.hpp
	Gives the unscaled formulas for several probability distributions.

file	tsgDreamLikelihoodCore.hpp
	The interface mother-class for the likelihood classes.

file	tsgDreamLikelyGaussian.hpp
	Several likelihood implementations based on Gaussian noise.

Detailed Description

DREAM: The DiffeRential Evolution Adaptive Metropolis is a method to draw samples from an arbitrary probability distribution defined by an arbitrary non-negative function (not necessarily normalized to integrate to 1). In the Tasmanian DREAM module, the samples (and the history) are stored in a TasDREAM::TasmanianDREAM state object which also defines the number of samples and the number of dimensions of the input space. The sampling is performed by the TasDREAM::SampleDREAM() template that takes an initialized state and several callable objects that describe the geometry of the domain and the parameters of the sampling, e.g., number of iterations.

Bayesian Inference: One of the most common applications for DREAM is in the context of Bayesian inference, where the probability distribution is comprised of a model, likelihood and prior. The three components can be combined together with the TasDREAM::posterior() template, which returns a callable object that represents the probability distribution.

Modules