nested_sampler

jaxns.nested_sampler

Submodules

Package Contents

class TerminationCondition[source]

Bases: NamedTuple

Contains the termination conditions for the nested sampling run.

Parameters:

  • ess – The effective sample size, if the ESS (Kish’s estimate) is greater than this the run will terminate.

  • evidence_uncert – The uncertainty in the evidence, if the uncertainty is less than this the run will terminate.

  • live_evidence_frac – Depreceated use dlogZ.

  • dlogZ – Terminate if log(Z_current + Z_remaining) - log(Z_current) < dlogZ. Default log(1 + 1e-2)

  • max_samples – Terminate if the number of samples exceeds this.

  • max_num_likelihood_evaluations – Terminate if the number of likelihood evaluations exceeds this.

  • log_L_contour – Terminate if this log(L) contour is reached. A contour is reached if any dead point has log(L) > log_L_contour. Uncollected live points are not considered.

  • efficiency_threshold – Terminate if the efficiency (num_samples / num_likelihood_evaluations) is less than this, for the last shrinkage iteration.

ess: FloatArray | None
evidence_uncert: FloatArray | None
live_evidence_frac: FloatArray | None
dlogZ: FloatArray | None
max_samples: IntArray | None
max_num_likelihood_evaluations: IntArray | None
log_L_contour: FloatArray | None
efficiency_threshold: FloatArray | None
__and__(other)[source]
__or__(other)[source]
class StandardStaticNestedSampler(init_efficiency_threshold, sampler, num_live_points, model, max_samples, num_parallel_workers=1, verbose=False)[source]

Bases: jaxns.nested_sampler.bases.BaseAbstractNestedSampler

A static nested sampler that uses a fixed number of live points. This uses a uniform sampler to generate the initial set of samples down to an efficiency threshold, then uses a provided sampler to generate the rest of the samples until the termination condition is met.

Initialise the static nested sampler.

Parameters:

  • init_efficiency_threshold (float) – the efficiency threshold to use for the initial uniform sampling. If 0 then turns it off.

  • sampler (jaxns.samplers.bases.BaseAbstractSampler) – the sampler to use after the initial uniform sampling.

  • num_live_points (int) – the number of live points to use.

  • model (jaxns.framework.bases.BaseAbstractModel) – the model to use.

  • max_samples (int) – the maximum number of samples to take.

  • num_parallel_workers (int) – number of parallel workers to use. Defaults to 1. Experimental feature.

  • verbose (bool) – whether to log as we go.

__repr__()[source]