diff --git a/ml4gw/waveforms/generator.py b/ml4gw/waveforms/generator.py
index fef52c4..5f94da7 100644
--- a/ml4gw/waveforms/generator.py
+++ b/ml4gw/waveforms/generator.py
@@ -1,4 +1,4 @@
-from typing import Callable, Dict, Tuple
+from typing import Callable, Dict, Optional, Tuple
 
 import torch
 from jaxtyping import Float
@@ -14,15 +14,41 @@
 
 
 class ParameterSampler(torch.nn.Module):
-    def __init__(self, **parameters: Callable) -> None:
+    def __init__(
+        self,
+        conversion_function: Optional[Callable] = None,
+        **params: Callable,
+    ):
+        """
+        A class for sampling parameters from a prior distribution
+
+        Args:
+            conversion_function:
+                A callable that takes a dictionary of sampled parameters
+                and returns a dictionary of transformed parameters
+            **params:
+                A dictionary of parameter samplers that take an integer N
+                and return a tensor of shape (N, ...) representing
+                samples from the prior distribution
+        """
         super().__init__()
-        self.parameters = parameters
+        self.params = params
+        self.conversion_function = conversion_function or (lambda x: x)
 
     def forward(
         self,
         N: int,
-    ) -> Dict[str, Float[Tensor, " {N}"]]:
-        return {k: v.sample((N,)) for k, v in self.parameters.items()}
+        device: str = "cpu",
+    ):
+        # sample parameters from prior
+        parameters = {
+            k: v.sample((N,)).to(device) for k, v in self.params.items()
+        }
+        # perform any necessary conversions
+        # to from sampled parameters to
+        # waveform generation parameters
+        parameters = self.conversion_function(parameters)
+        return parameters
 
 
 class TimeDomainCBCWaveformGenerator(torch.nn.Module):