From 617ab30272bdb0c571fa413c5f29156b6174095f Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Sun, 23 Dec 2018 12:32:54 +0000
Subject: [PATCH] update comments
---
.../marginalized_likelihood.py | 17 +++++++++++++++--
1 file changed, 15 insertions(+), 2 deletions(-)
diff --git a/examples/injection_examples/marginalized_likelihood.py b/examples/injection_examples/marginalized_likelihood.py
index 06ef88b67..f78861e9a 100644
--- a/examples/injection_examples/marginalized_likelihood.py
+++ b/examples/injection_examples/marginalized_likelihood.py
@@ -2,6 +2,9 @@
"""
Tutorial to demonstrate how to improve the speed and efficiency of parameter
estimation on an injected signal using time, phase and distance marginalisation.
+
+We also demonstrate how the posterior distribution for the marginalised
+parameter can be recovered in post-processing.
"""
from __future__ import division, print_function
import bilby
@@ -39,6 +42,10 @@ ifos.inject_signal(waveform_generator=waveform_generator,
# Set up prior
priors = bilby.gw.prior.BBHPriorDict()
+priors['geocent_time'] = bilby.core.prior.Uniform(
+ minimum=injection_parameters['geocent_time'] - 1,
+ maximum=injection_parameters['geocent_time'] + 1,
+ name='geocent_time', latex_label='$t_c$', unit='$s$')
# These parameters will not be sampled
for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra',
'dec']:
@@ -46,7 +53,7 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra',
# Initialise GravitationalWaveTransient
# Note that we now need to pass the: priors and flags for each thing that's
-# being marginalised. A lookup table is used fro distance marginalisation which
+# being marginalised. A lookup table is used for distance marginalisation which
# takes a few minutes to build.
likelihood = bilby.gw.GravitationalWaveTransient(
interferometers=ifos, waveform_generator=waveform_generator, priors=priors,
@@ -54,7 +61,13 @@ likelihood = bilby.gw.GravitationalWaveTransient(
time_marginalization=True)
# Run sampler
+# Note that we've added an additional argument `conversion_function`, this is
+# a function that is applied to the posterior. Here it generates many additional
+# parameters, e.g., source frame masses and effective spin parameters. It also
+# reconstructs posterior distributions for the parameters which were
+# marginalised over in the likelihood.
result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, outdir=outdir, label=label)
+ injection_parameters=injection_parameters, outdir=outdir, label=label,
+ conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
result.plot_corner()
--
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