BaseEnsembleSampler

class BaseEnsembleSampler(**kwargs)

Bases: InternalSampler

__init__(**kwargs)

Initialize the internal sampler.

Importantely this sets up the sampler_kwargs that is being passed to each .sample() call

Parameters:

kwargsdict

A dictionary of additional method-specific parameters. This common keywords:

nonboundedarray

Array of boolean values indicating which dimensions are non-bounded.

periodicarray

Array of boolean values indicating which dimensions are periodic.

reflectivearray

Array of boolean values indicating which dimensions are reflective.

ndim: int

Number of dimensions.

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__(**kwargs)	Initialize the internal sampler.
prepare_sampler([loglstar, points, axes, ...])	Prepare the list of arguments for sampling.

prepare_sampler(loglstar=None, points=None, axes=None, seeds=None, prior_transform=None, loglikelihood=None, nested_sampler=None)

Prepare the list of arguments for sampling.

Parameters:

loglstarfloat

Ln(likelihood) bound.

points~numpy.ndarray with shape (n, ndim)

Initial sample points.

axes~numpy.ndarray with shape (ndim, ndim)

Axes used to propose new points.

seeds~numpy.ndarray with shape (n,)

Random number generator seeds.

prior_transformfunction

Function transforming a sample from the a unit cube to the parameter space of interest according to the prior.

loglikelihoodfunction

Function returning ln(likelihood) given parameters as a 1-d ~numpy array of length ndim.

nested_sampler~dynesty.samplers.Sampler

The nested sampler object used to sample.

Returns:

arglist:

List of SamplerArgument objects containing the parameters needed for sampling.