diff --git a/nmma/gw/likelihood.py b/nmma/gw/likelihood.py
index 45d5078f..d907fe0d 100644
--- a/nmma/gw/likelihood.py
+++ b/nmma/gw/likelihood.py
@@ -8,7 +8,6 @@
     )
 
 from bilby.gw.likelihood import GravitationalWaveTransient, ROQGravitationalWaveTransient
-from bilby.gw.likelihood.relative import RelativeBinningGravitationalWaveTransient
 from bilby.core.likelihood import Likelihood
 from bilby.core.prior import Interped
 
@@ -92,20 +91,16 @@ class GravitationalWaveTransientLikelihoodwithEOS(Likelihood):
           this is the default
         - e.g., "H1": sample in the time of arrival at H1
 
-    fiducial_parameters: dictionary, fiducial parameters for relative binning
-        reference waveform
-    epsilon: float, sets the precision of the binning for relative binning
     """
 
     def __init__(self, interferometers, waveform_generator,
                  eos_path, Neos, eos_weight_path, binary_type, gw_likelihood_type,
-                 priors, fiducial_parameters, epsilon, with_eos=True,
+                 priors, with_eos=True,
                  roq_weights=None, roq_params=None, roq_scale_factor=None,
                  time_marginalization=False, distance_marginalization=False,
                  phase_marginalization=False, distance_marginalization_lookup_table=None,
                  jitter_time=True, reference_frame="sky", time_reference="geocenter"):
 
-
         # construct the eos prior
         if with_eos:
             xx = np.arange(0, Neos + 1)
@@ -149,11 +144,6 @@ def __init__(self, interferometers, waveform_generator,
                                              roq_scale_factor=roq_scale_factor))
             GWLikelihood = ROQGravitationalWaveTransient(**gw_likelihood_kwargs)
 
-        elif gw_likelihood_type == 'RelativeBinningGravitationalWaveTransient':
-
-            gw_likelihood_kwargs.update(dict(fiducial_parameters=fiducial_parameters, epsilon=epsilon))
-            GWLikelihood = RelativeBinningGravitationalWaveTransient(**gw_likelihood_kwargs)
-
         super(GravitationalWaveTransientLikelihoodwithEOS, self).__init__(parameters={})
         self.parameter_conversion = parameter_conversion
         self.GWLikelihood = GWLikelihood
diff --git a/nmma/pbilby/analysis/likelihood.py b/nmma/pbilby/analysis/likelihood.py
index 9b35218c..4f2596b3 100644
--- a/nmma/pbilby/analysis/likelihood.py
+++ b/nmma/pbilby/analysis/likelihood.py
@@ -2,7 +2,6 @@
 
 import numpy as np
 import pandas as pd
-import ast
 
 from bilby.core.utils import logger
 from bilby.core.prior import Interped
@@ -48,7 +47,6 @@ def reorder_loglikelihoods(unsorted_loglikelihoods, unsorted_samples, sorted_sam
 
 
 def roq_likelihood_kwargs(args):
-    
     """Return the kwargs required for the ROQ setup
 
     Parameters
@@ -88,31 +86,7 @@ def roq_likelihood_kwargs(args):
         kwargs["quadratic_matrix"] = args.roq_quadratic_matrix
     return kwargs
 
-def relbin_likelihood_kwargs(args):
 
-    """Return the kwargs required for the relative binning setup
-
-    Parameters
-    ----------
-    args: Namespace
-        The parser arguments
-
-    Returns
-    -------
-    kwargs: dict
-        A dictionary of the required kwargs
-
-    """
-
-    kwargs = dict(
-        fiducial_parameters = ast.literal_eval(args.fiducial_parameters),
-        epsilon = args.epsilon
-        )
-
-    return kwargs
-   
-    
-    
 def setup_nmma_likelihood(
     interferometers, waveform_generator, light_curve_data, priors, args
 ):
@@ -240,10 +214,6 @@ def setup_nmma_likelihood(
         likelihood_kwargs.pop("time_marginalization", None)
         likelihood_kwargs.pop("jitter_time", None)
         likelihood_kwargs.update(roq_likelihood_kwargs(args))
-
-    elif args.likelihood_type == "RelativeBinningGravitationalWaveTransient":
-        likelihood_kwargs.update(relbin_likelihood_kwargs(args))
-        
     else:
         raise ValueError("Unknown Likelihood class {}")
 
@@ -357,10 +327,6 @@ def setup_nmma_gw_likelihood(interferometers, waveform_generator, priors, args):
         likelihood_kwargs.pop("time_marginalization", None)
         likelihood_kwargs.pop("jitter_time", None)
         likelihood_kwargs.update(roq_likelihood_kwargs(args))
-
-    elif args.likelihood_type == "RelativeBinningGravitationalWaveTransient":
-        likelihood_kwargs.update(relbin_likelihood_kwargs(args))
-
     else:
         raise ValueError("Unknown Likelihood class {}")
 
diff --git a/nmma/pbilby/parser/shared.py b/nmma/pbilby/parser/shared.py
index 417c3364..57ab72ef 100644
--- a/nmma/pbilby/parser/shared.py
+++ b/nmma/pbilby/parser/shared.py
@@ -209,7 +209,7 @@ def _add_eos_settings_to_parser(parser):
 
     return parser
 
-    
+
 def _add_Hubble_settings_to_parser(parser):
     H0_input_parser = parser.add_argument_group(
         title="Hubble input arguments", description="Specify Hubble inputs"