TransportMaps.Samplers.IndependentSamplers

Module Contents

Classes

ImportanceSampler	Importance sampler of distribution d, with biasing distribution d_bias
RejectionSampler	Rejection sampler of distribution d, with biasing distribution d_bias

class TransportMaps.Samplers.IndependentSamplers.ImportanceSampler(d, d_bias)

Bases: TransportMaps.Samplers.SamplerBase.Sampler

Importance sampler of distribution d, with biasing distribution d_bias

Parameters:

• d (Distributions.Distribution) – distribution to sample from

• d_bias (Distributions.Distribution) – biasing distribution

rvs(m, mpi_pool_tuple=(None, None))

Generate \(m\) samples and importance weights from the distribution

Parameters:

m (int) – number of samples to generate

Returns:

(tuple (ndarray [\(m,d\)], ndarray [\(m\)])) – list of points and weights

class TransportMaps.Samplers.IndependentSamplers.RejectionSampler(d, d_bias, factor)

Bases: TransportMaps.Samplers.SamplerBase.Sampler

Rejection sampler of distribution d, with biasing distribution d_bias

Parameters:

• d (Distributions.Distribution) – distribution to sample from

• d_bias (Distributions.Distribution) – biasing distribution

• factor (float) – scaling factor to allow the biasing distribution to dominate

rvs(m, *args, **kwargs)

Generate \(m\) samples and importance weights from the distribution

Parameters:

m (int) – number of samples to generate

Optional Kwags:

maxtrialmul (int): multiplicator of m giving the number of maximum trial samples.

Default is 100.

Returns:

(tuple (ndarray [\(m,d\)], ndarray [\(m\)])) – list of points and weights