bilby.gw.conversion.generate_posterior_samples_from_marginalized_likelihood

bilby.gw.conversion.generate_posterior_samples_from_marginalized_likelihood(samples, likelihood, npool=1, block=10, use_cache=True)

Reconstruct the distance posterior from a run which used a likelihood which explicitly marginalised over time/distance/phase.

See Eq. (C29-C32) of https://arxiv.org/abs/1809.02293

Parameters:

samples: DataFrame

Posterior from run with a marginalised likelihood.

likelihood: bilby.gw.likelihood.GravitationalWaveTransient

Likelihood used during sampling.

npool: int, (default=1)

If given, perform generation (where possible) using a multiprocessing pool

block: int, (default=10)

Size of the blocks to use in multiprocessing

use_cache: bool, (default=True)

If true, cache the generation so that reconstuction can begin from the cache on restart.

Returns:

sample: DataFrame

Returns the posterior with new samples.