diff --git a/pymc3/distributions/multivariate.py b/pymc3/distributions/multivariate.py
index 3b7196682d..e9b9a87e99 100755
--- a/pymc3/distributions/multivariate.py
+++ b/pymc3/distributions/multivariate.py
@@ -486,23 +486,9 @@ class Dirichlet(Continuous):
 
     def __init__(self, a, transform=transforms.stick_breaking,
                  *args, **kwargs):
-
-        if not isinstance(a, pm.model.TensorVariable):
-            if not isinstance(a, list) and not isinstance(a, np.ndarray):
-                raise TypeError(
-                    'The vector of concentration parameters (a) must be a python list '
-                    'or numpy array.')
-            a = np.array(a)
-            if (a <= 0).any():
-               raise ValueError("All concentration parameters (a) must be > 0.")
-
-        shape = np.atleast_1d(a.shape)[-1]
-
-        kwargs.setdefault("shape", shape)
         super().__init__(transform=transform, *args, **kwargs)
 
         self.size_prefix = tuple(self.shape[:-1])
-        self.k = tt.as_tensor_variable(shape)
         self.a = a = tt.as_tensor_variable(a)
         self.mean = a / tt.sum(a)
 
@@ -569,14 +555,13 @@ def logp(self, value):
         -------
         TensorVariable
         """
-        k = self.k
         a = self.a
 
         # only defined for sum(value) == 1
         return bound(tt.sum(logpow(value, a - 1) - gammaln(a), axis=-1)
                      + gammaln(tt.sum(a, axis=-1)),
                      tt.all(value >= 0), tt.all(value <= 1),
-                     k > 1, tt.all(a > 0),
+                     np.logical_not(a.broadcastable), tt.all(a > 0),
                      broadcast_conditions=False)
 
     def _repr_latex_(self, name=None, dist=None):