diff --git a/pymc3/distributions/distribution.py b/pymc3/distributions/distribution.py
index 8178ae0d22..057f7b7904 100644
--- a/pymc3/distributions/distribution.py
+++ b/pymc3/distributions/distribution.py
@@ -92,10 +92,40 @@ def __new__(cls, name, *args, **kwargs):
         if not isinstance(name, string_types):
             raise TypeError(f"Name needs to be a string but got: {name}")
 
-        data = kwargs.pop("observed", None)
-        cls.data = data
-        if isinstance(data, ObservedRV) or isinstance(data, FreeRV):
-            raise TypeError("observed needs to be data but got: {}".format(type(data)))
+        observed_data = kwargs.pop("observed", None)
+        if isinstance(observed_data, ObservedRV) or isinstance(observed_data, FreeRV):
+            raise TypeError("observed needs to be data but got: {}".format(type(observed_data)))
+        given_data = kwargs.pop("givens", None)
+        if given_data is None:
+            cls.data = observed_data
+        elif not isinstance(given_data, dict):
+            raise TypeError(f"givens needs to be of type dict but got: {type(givens)}")
+        elif observed_data is None:
+            cls.data = given_data
+        elif isinstance(observed_data, dict):
+            non_data_obs = {
+                key: type(value)
+                for key, value in observed_data.items()
+                if isinstance(value, ObservedRV) or isinstance(value, FreeRV)
+            }
+            if non_data_obs:
+                raise TypeError(
+                    f"All values in observed dict need to be data but got: {non_data_obs}. "
+                    "You may want to use the givens argument in DensityDist."
+                )
+            intersection = given_data.keys() & observed_data.keys()
+            if intersection:
+                raise ValueError(
+                    f"{intersection} keys found in both givens and observed dicts but "
+                    "they can not have repeated keys"
+                )
+            cls.data = {**observed_data, **given_data}
+        else:
+            raise ValueError(
+                "If both observed and givens argument are present, observed needs to "
+                f"be a dict but got: {type(observed_data)}"
+            )
+        data = cls.data
         total_size = kwargs.pop("total_size", None)
 
         dims = kwargs.pop("dims", None)
@@ -119,7 +149,7 @@ def __new__(cls, name, *args, **kwargs):
             dist = cls.dist(*args, **kwargs, shape=shape)
         else:
             dist = cls.dist(*args, **kwargs)
-        return model.Var(name, dist, data, total_size, dims=dims)
+        return model.Var(name, dist, data, total_size, dims=dims, givens=given_data)
 
     def __getnewargs__(self):
         return (_Unpickling,)
@@ -403,6 +433,8 @@ def __init__(
             If ``True``, the shape of the random samples generate in the
             ``random`` method is checked with the expected return shape. This
             test is only performed if ``wrap_random_with_dist_shape is False``.
+        givens : dict, optional
+            Model variables on which the DensityDist is conditioned.
         args, kwargs: (Optional)
             These are passed to the parent class' ``__init__``.
 
@@ -506,24 +538,6 @@ def __init__(
             the returned array of samples. It is the user's responsibility to
             wrap the callable to make it comply with PyMC3's interpretation
             of ``size``.
-
-
-            .. code-block:: python
-
-                with pm.Model():
-                    mu = pm.Normal('mu', 0 , 1)
-                    normal_dist = pm.Normal.dist(mu, 1, shape=3)
-                    dens = pm.DensityDist(
-                        'density_dist',
-                        normal_dist.logp,
-                        observed=np.random.randn(100, 3),
-                        shape=3,
-                        random=stats.norm.rvs,
-                        pymc3_size_interpretation=False, # Is True by default
-                    )
-                    prior = pm.sample_prior_predictive(10)['density_dist']
-                assert prior.shape == (10, 100, 3)
-
         """
         if dtype is None:
             dtype = theano.config.floatX
diff --git a/pymc3/model.py b/pymc3/model.py
index 393c4d2f6a..b343074bde 100644
--- a/pymc3/model.py
+++ b/pymc3/model.py
@@ -1109,7 +1109,7 @@ def add_coords(self, coords):
             else:
                 self.coords[name] = coords[name]
 
-    def Var(self, name, dist, data=None, total_size=None, dims=None):
+    def Var(self, name, dist, data=None, total_size=None, dims=None, givens=None):
         """Create and add (un)observed random variable to the model with an
         appropriate prior distribution.
 
@@ -1161,6 +1161,7 @@ def Var(self, name, dist, data=None, total_size=None, dims=None):
                 var = MultiObservedRV(
                     name=name,
                     data=data,
+                    givens=givens,
                     distribution=dist,
                     total_size=total_size,
                     model=self,
@@ -1834,7 +1835,7 @@ class MultiObservedRV(Factor):
     Potentially partially observed.
     """
 
-    def __init__(self, name, data, distribution, total_size=None, model=None):
+    def __init__(self, name, data, distribution, total_size=None, model=None, givens=None):
         """
         Parameters
         ----------
@@ -1850,6 +1851,7 @@ def __init__(self, name, data, distribution, total_size=None, model=None):
         self.data = {
             name: as_tensor(data, name, model, distribution) for name, data in data.items()
         }
+        self.givens = givens
 
         self.missing_values = [
             datum.missing_values for datum in self.data.values() if datum.missing_values is not None