diff --git a/examples/injection_examples/basic_tutorial.py b/examples/injection_examples/basic_tutorial.py
index 00e8403b272594f1bde053058d1088a3394f107d..39a3d171bb6519f7359296fe938a28c618153758 100644
--- a/examples/injection_examples/basic_tutorial.py
+++ b/examples/injection_examples/basic_tutorial.py
@@ -45,21 +45,18 @@ IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
     sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1']]
 
 # Set up prior, which is a dictionary
-priors = dict()
 # By default we will sample all terms in the signal models.  However, this will take a long time for the calculation,
 # so for this example we will set almost all of the priors to be equall to their injected values.  This implies the
 # prior is a delta function at the true, injected value.  In reality, the sampler implementation is smart enough to
 # not sample any parameter that has a delta-function prior.
-
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'dec', 'geocent_time', 'phase']:
-    priors[key] = injection_parameters[key]
-
 # The above list does *not* include mass_1, mass_2, iota and luminosity_distance, which means those are the parameters
 # that will be included in the sampler.  If we do nothing, then the default priors get used.
-priors['luminosity_distance'] = tupak.core.prior.create_default_prior(name='luminosity_distance')
-priors['geocent_time'] = tupak.core.prior.Uniform(injection_parameters['geocent_time'] - 1,
-                                                  injection_parameters['geocent_time'] + 1,
-                                            'geocent_time')
+priors = tupak.gw.prior.BBHPriorSet()
+priors['geocent_time'] = tupak.core.prior.Uniform(
+    minimum=injection_parameters['geocent_time'] - 1, maximum=injection_parameters['geocent_time'] + 1,
+    name='geocent_time', latex_label='$t_c$')
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra', 'dec', 'geocent_time', 'phase']:
+    priors[key] = injection_parameters[key]
 
 # Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
 likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
diff --git a/examples/injection_examples/change_sampled_parameters.py b/examples/injection_examples/change_sampled_parameters.py
index 5ef99386ea412aebb9fa910d3a7ae58c5c3f247e..711e195c097f3b54d613fb49e8d82b88cae1a0c7 100644
--- a/examples/injection_examples/change_sampled_parameters.py
+++ b/examples/injection_examples/change_sampled_parameters.py
@@ -27,7 +27,7 @@ waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
     sampling_frequency=sampling_frequency, time_duration=time_duration,
     frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
     parameter_conversion=tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters,
-    non_standard_sampling_parameter_keys=['chirp_mass', 'mass_ratio', 'cos_iota'],
+    non_standard_sampling_parameter_keys=['chirp_mass', 'mass_ratio'],
     parameters=injection_parameters)
 hf_signal = waveform_generator.frequency_domain_strain()
 
@@ -37,11 +37,15 @@ IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
     sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
 
 # Set up prior
-priors = dict()
+priors = tupak.gw.prior.BBHPriorSet()
+priors.pop('mass_1')
+priors.pop('mass_2')
+priors['chirp_mass'] = tupak.prior.Uniform(name='chirp_mass', latex_label='$m_c$', minimum=13, maximum=45)
+priors['mass_ratio'] = tupak.prior.Uniform(name='mass_ratio', latex_label='$q$', minimum=0.1, maximum=1)
 # These parameters will not be sampled
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'ra', 'dec', 'geocent_time']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra', 'dec', 'geocent_time', 'phase']:
     priors[key] = injection_parameters[key]
-priors['luminosity_distance'] = tupak.core.prior.create_default_prior(name='luminosity_distance')
+print(priors)
 
 # Initialise GravitationalWaveTransient
 likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
diff --git a/examples/injection_examples/marginalized_likelihood.py b/examples/injection_examples/marginalized_likelihood.py
index c3c4f29ef2d0b0706542974fb8f90208b9865a9a..5ca2ed1b8834c6b190015bb7a6fa237784f03441 100644
--- a/examples/injection_examples/marginalized_likelihood.py
+++ b/examples/injection_examples/marginalized_likelihood.py
@@ -31,7 +31,7 @@ IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
     sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
 
 # Set up prior
-priors = dict()
+priors = tupak.gw.prior.BBHPriorSet()
 # These parameters will not be sampled
 for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'iota', 'ra', 'dec', 'geocent_time']:
     priors[key] = injection_parameters[key]