diff --git a/kinisi/arrhenius.py b/kinisi/arrhenius.py
index 66670b9e..67a9af9a 100644
--- a/kinisi/arrhenius.py
+++ b/kinisi/arrhenius.py
@@ -15,7 +15,6 @@
 from typing import List, Union, Tuple
 import numpy as np
 from uravu.relationship import Relationship
-from uravu.distribution import Distribution
 from scipy.constants import R, N_A, eV
 
 # Convert R to eV
@@ -68,7 +67,7 @@ def distribution(self) -> np.ndarray:
         """
         return self.function(self.x[:, np.newaxis], self.flatchain[:, 0], self.flatchain[:, 1])
 
-    def extrapolate(self, extrapolated_temperature: Union[float, List[float], np.ndarray], posterior_predictive_kwargs=None) -> 'uravu.distribution.Distribution':
+    def extrapolate(self, extrapolated_temperature: float) -> 'uravu.distribution.Distribution':
         """
         Extrapolate the diffusion coefficient to some un-investigated value. This can also be
         used for interpolation.
@@ -76,13 +75,7 @@ def extrapolate(self, extrapolated_temperature: Union[float, List[float], np.nda
         :param: Temperature to return diffusion coefficient at.
         :return: Diffusion coefficient at extrapolated temperature.
         """
-        if posterior_predictive_kwargs is None:
-            posterior_predictive_kwargs = {}
-        if isinstance(extrapolated_temperature, (float, int, complex)) :
-            extrapolated_temperature = np.array([extrapolated_temperature])
-        if isinstance(extrapolated_temperature, List):
-            extrapolated_temperature = np.array(extrapolated_temperature)
-        return self.posterior_predictive(abscissa_values=extrapolated_temperature, **posterior_predictive_kwargs)
+        return self.function(extrapolated_temperature, self.flatchain[:, 0], self.flatchain[:, 1])
 
 
 def arrhenius(abscissa: np.ndarray, activation_energy: float, prefactor: float) -> np.ndarray:
@@ -155,7 +148,7 @@ def distribution(self) -> np.ndarray:
         """
         return self.function(self.x[:, np.newaxis], self.flatchain[:, 0], self.flatchain[:, 1], self.flatchain[:, 2])
 
-    def extrapolate(self, extrapolated_temperature: Union[float, List[float], np.ndarray], posterior_predictive_kwargs=None) -> 'uravu.distribution.Distribution':
+    def extrapolate(self, extrapolated_temperature: float) -> 'uravu.distribution.Distribution':
         """
         Extrapolate the diffusion coefficient to some un-investigated value. This can also be
         used for interpolation.
@@ -163,13 +156,7 @@ def extrapolate(self, extrapolated_temperature: Union[float, List[float], np.nda
         :param: Temperature to return diffusion coefficient at.
         :return: Diffusion coefficient at extrapolated temperature.
         """
-        if posterior_predictive_kwargs is None:
-            posterior_predictive_kwargs = {}
-        if isinstance(extrapolated_temperature, (float, int, complex)) :
-            extrapolated_temperature = np.array([extrapolated_temperature])
-        if isinstance(extrapolated_temperature, List):
-            extrapolated_temperature = np.array(extrapolated_temperature)
-        return self.posterior_predictive(abscissa_values=extrapolated_temperature, **posterior_predictive_kwargs)
+        return self.function(extrapolated_temperature, self.flatchain[:, 0], self.flatchain[:, 1], self.flatchain[:, 2])
 
 
 def super_arrhenius(abscissa: np.ndarray, activation_energy: float, prefactor: float, t_zero: float) -> np.ndarray: