nessai.proposal.rejection

Proposal method for initial sampling when priors are not analytical.

Module Contents

Classes

RejectionProposal

Object for rejection sampling from the priors.

class nessai.proposal.rejection.RejectionProposal(*args, **kwargs)

Bases: nessai.proposal.analytic.AnalyticProposal

Object for rejection sampling from the priors.

See parent for explanation of arguments and keyword arguments.

Will be used when nessai is called with analytic_priors=False. This is the default behaviour.

Relies on nessai.model.Model.new_point() to draw new points and nessai.model.Model.new_point_log_prob() when computing the probability of each new point.

draw_proposal(N=None)

Draw new point(s).

Parameters:

Nint, optional

Number of samples to draw. If not specified poolsize will be used.

Returns:

structured_array

Array of N new points

log_proposal(x)

Log proposal probability. Calls nessai.model.Model.new_point_log_prob()

Parameters:

xstructured_array

Array of new points

Returns:

numpy.ndarray

Array of log-probabilities.

compute_weights(x, return_log_prior=False)

Get weights for the samples.

Computes the log weights for rejection sampling sampling but does not normalize the weights.

Parameters:

xstructured_array

Array of points

return_log_prior: bool

If true, the log-prior probability is also returned.

Returns:

log_wnumpy.ndarray

Array of log-weights rescaled such that the maximum value is zero.

populate(N=None)

Populate the pool by drawing from the proposal distribution and using rejection sampling.

Will also evaluate the likelihoods if the proposal contains a multiprocessing pool.

Parameters:

Nint, optional

Number of samples to draw. Not all samples will be accepted to the number of samples saved will be less than N. If not specified poolsize will be used.