IID generates a set of independently and identically distributed samples from the input distribution.

Importance sampling generates len(batch) samples from the proposal distribution, and stores the locations and importance sampling weights in place.

LatinHypercube generates len(batch) samples using Latin hypercube sampling from the given distribution.

MetropolisHastings generates len(batch) samples using the Metropolis Hastings algorithm (http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm), with the given target and proposal distributions, starting at the intial location and storing the results in-place into samples.

NewWeighted returns a Weighted for the weights w.

Rejection generates len(batch) samples using the rejection sampling algorithm and stores them in place into samples.

ErrRejection is returned when the constant in Rejection is not sufficiently high.

IIDer is a wrapper around the IID sample generation method.

Importancer is a wrapper around the Importance sampling generation method.

LatinHypercuber is a wrapper around the LatinHypercube sampling generation method.

MetropolisHastingser is a wrapper around the MetropolisHastings sampling type.

Rejectioner is a wrapper around the Rejection sampling generation procedure.

SampleUniformWeighted wraps a Sampler type to create a WeightedSampler where all weights are equal.

Weighted provides sampling without replacement from a collection of items with non-uniform probability.

MHProposal defines a proposal distribution for Metropolis Hastings.

Sampler generates a batch of samples according to the rule specified by the implementing type.

WeightedSampler generates a batch of samples and their relative weights according to the rule specified by the implementing type.