diff --git a/bilby/core/prior.py b/bilby/core/prior.py
index 276fd0e4bfc23c0c5c664e2cb247cb0cfd2479e5..f5b87d6f21d55e73fc8901e70e52bdbef80f9b1f 100644
--- a/bilby/core/prior.py
+++ b/bilby/core/prior.py
@@ -1941,8 +1941,8 @@ class MultivariateGaussian(object):
         Check if all requested parameters have been filled.
         """
 
-        return np.any([val is None for val in
-                       self.requested_parameters.values()])
+        return not np.any([val is None for val in
+                          self.requested_parameters.values()])
 
     def reset_request(self):
         """
@@ -1957,8 +1957,8 @@ class MultivariateGaussian(object):
         Check is all the rescaled parameters have been filled.
         """
 
-        return np.any([val is None for val in
-                       self.rescale_parameters.values()])
+        return not np.any([val is None for val in
+                          self.rescale_parameters.values()])
 
     def reset_rescale(self):
         """
@@ -2060,7 +2060,7 @@ class MultivariateGaussian(object):
         self.mvn.append(scipy.stats.multivariate_normal(mean=self.mus[-1],
                                                         cov=self.covs[-1]))
 
-    def rescale(self, value):
+    def rescale(self, value, mode=None):
         """
         Rescale from a unit hypercube to multivariate Gaussian. Note that no
         bounds are applied in the rescale function.
@@ -2070,6 +2070,9 @@ class MultivariateGaussian(object):
         value: array
             A vector sample (one for each parameter) drawn from a uniform
             distribution between 0 and 1.
+        mode: int
+            Specify which mode to sample from. If not set then a mode is
+            chosen randomly based on its weight.
 
         Returns
         -------
@@ -2079,21 +2082,31 @@ class MultivariateGaussian(object):
         """
 
         # pick a mode (with a probability given by their weights)
-        imode = np.argwhere(self.cumweights - np.random.rand() > 0)[0][0]
+        if mode is None:
+            if self.nmodes == 1:
+                mode = 0
+            else:
+                mode = np.argwhere(self.cumweights - np.random.rand() > 0)[0][0]
 
         # draw points from unit variance, uncorrelated Gaussian
-        samp = erfinv(2. * value - 1) * 2. ** 0.5
+        samp = erfinv(2. * np.asarray(value) - 1) * 2. ** 0.5
 
         # rotate and scale to the multivariate normal shape
-        samp = self.mus[imode] + self.sigmas[imode] * np.einsum('j,kj->k',
-                                                      samp * self.sqeigvalues[imode],
-                                                      self.eigvectors[imode])
+        samp = self.mus[mode] + self.sigmas[mode] * np.einsum('j,kj->k',
+                                                    samp * self.sqeigvalues[mode],
+                                                    self.eigvectors[mode])
 
         return samp
 
-    def sample(self):
+    def sample(self, mode=None):
         """
-        Draw a sample from the multivariate Gaussian.
+        Draw, and set, a sample from the multivariate Gaussian.
+
+        Parameters
+        ----------
+        mode: int
+            Specify which mode to sample from. If not set then a mode is
+            chosen randomly based on its weight.
         """
 
         # samples drawn from unit variance uncorrelated multivariate Gaussian
@@ -2102,7 +2115,7 @@ class MultivariateGaussian(object):
             # sample the multivariate Gaussian keys
             vals = np.random.uniform(0, 1, len(self))
 
-            samp = self.rescale(vals)
+            samp = self.rescale(vals, mode=mode)
 
             # check sample is in bounds (otherwise perform another draw)
             outbound = False
@@ -2184,24 +2197,42 @@ class MultivariateGaussianPrior(Prior):
                        maximum=mvg.bounds[name][1])
         self.mvg = mvg
 
-    def rescale(self, val):
+    def rescale(self, val, mode=None):
         """
         Scale a unit hypercube sample to the prior.
+
+        Parameters
+        ----------
+        mode: int
+            Specify which mode to sample from. If not set then a mode is
+            chosen randomly based on its weight.
         """
 
         # add parameter value to multivariate Gaussian
         self.mvg.rescale_parameters[self.name] = val
 
         if self.mvg.filled_rescale():
-            samples = self.mvg.rescale(list(self.mvg.rescale_parameters.values()))
+            samples = self.mvg.rescale(list(self.mvg.rescale_parameters.values()),
+                                       mode=mode)
             self.mvg.reset_rescale()
             return samples
         else:
             return []  # return empty list
 
-    def sample(self, size=None):
+    def sample(self, size=None, mode=None):
         """
         Draw a sample from the prior.
+
+        Parameters
+        ----------
+        mode: int
+            Specify which mode to sample from. If not set then a mode is
+            chosen randomly based on its weight.
+
+        Returns
+        -------
+        float:
+            A sample from the prior paramter.
         """
 
         if self.name in self.mvg.sampled_parameters:
@@ -2211,7 +2242,7 @@ class MultivariateGaussianPrior(Prior):
 
         if len(self.mvg.current_sample) == 0:
             # generate a sample
-            self.mvg.sample()
+            self.mvg.sample(mode=mode)
         
         sample = self.mvg.current_sample[self.name]