diff --git a/tupak/result.py b/tupak/result.py
index d14d37561477bb9d6ce8784f43512d85ab94316f..19aa9a1ea37de044570c5669a2050551a645d957 100644
--- a/tupak/result.py
+++ b/tupak/result.py
@@ -200,7 +200,7 @@ class Result(dict):
         data_frame = pd.DataFrame(self.samples, columns=self.search_parameter_keys)
         self.posterior = data_frame
         for key in self.fixed_parameter_keys:
-            self.posterior[key] = self.prior[key].sample(len(self.posterior))
+            self.posterior[key] = self.priors[key].sample(len(self.posterior))
 
     def construct_cbc_derived_parameters(self):
         """
diff --git a/tupak/sampler.py b/tupak/sampler.py
index d7bf4dc554bd842dd68ba948892ac95fd45d78a2..7e1bc58db77bca4256fb2c4b0b70385a3f963467 100644
--- a/tupak/sampler.py
+++ b/tupak/sampler.py
@@ -70,6 +70,7 @@ class Sampler(object):
         if result is None:
             self.__result = Result()
             self.__result.search_parameter_keys = self.__search_parameter_keys
+            self.__result.fixed_parameter_keys = self.__fixed_parameter_keys
             self.__result.parameter_labels = [
                 self.priors[k].latex_label for k in
                 self.__search_parameter_keys]
@@ -456,7 +457,6 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
         else:
             result.log_bayes_factor = result.logz - result.noise_logz
         result.injection_parameters = injection_parameters
-        result.fixed_parameter_keys = [key for key in priors if isinstance(key, prior.DeltaFunction)]
         result.priors = priors
         result.kwargs = sampler.kwargs
         result.samples_to_data_frame()