diff --git a/pymc/distributions/multivariate.py b/pymc/distributions/multivariate.py
index bc6a5da9094..aa5b2119cfa 100644
--- a/pymc/distributions/multivariate.py
+++ b/pymc/distributions/multivariate.py
@@ -238,7 +238,7 @@ class MvNormal(Continuous):
     rv_op = multivariate_normal
 
     @classmethod
-    def dist(cls, mu, cov=None, tau=None, chol=None, lower=True, **kwargs):
+    def dist(cls, mu=0, cov=None, *, tau=None, chol=None, lower=True, **kwargs):
         mu = pt.as_tensor_variable(mu)
         cov = quaddist_matrix(cov, chol, tau, lower)
         # PyTensor is stricter about the shape of mu, than PyMC used to be
@@ -358,7 +358,7 @@ class MvStudentT(Continuous):
     rv_op = mv_studentt
 
     @classmethod
-    def dist(cls, nu, *, Sigma=None, mu, scale=None, tau=None, chol=None, lower=True, **kwargs):
+    def dist(cls, nu, *, Sigma=None, mu=0, scale=None, tau=None, chol=None, lower=True, **kwargs):
         cov = kwargs.pop("cov", None)
         if cov is not None:
             warnings.warn(
diff --git a/tests/distributions/test_multivariate.py b/tests/distributions/test_multivariate.py
index 4fb69e92e34..3013cd15331 100644
--- a/tests/distributions/test_multivariate.py
+++ b/tests/distributions/test_multivariate.py
@@ -2300,7 +2300,11 @@ def test_mvnormal_no_cholesky_in_model_logp():
 
 
 def test_mvnormal_mu_convenience():
-    """Test that mu is broadcasted to the length of cov"""
+    """Test that mu is broadcasted to the length of cov and provided a default of zero"""
+    x = pm.MvNormal.dist(cov=np.eye(3))
+    mu = x.owner.inputs[3]
+    np.testing.assert_allclose(mu.eval(), np.zeros((3,)))
+
     x = pm.MvNormal.dist(mu=1, cov=np.eye(3))
     mu = x.owner.inputs[3]
     np.testing.assert_allclose(mu.eval(), np.ones((3,)))
@@ -2325,7 +2329,11 @@ def test_mvnormal_mu_convenience():
 
 
 def test_mvstudentt_mu_convenience():
-    """Test that mu is broadcasted to the length of scale"""
+    """Test that mu is broadcasted to the length of scale and provided a default of zero"""
+    x = pm.MvStudentT.dist(nu=4, scale=np.eye(3))
+    mu = x.owner.inputs[4]
+    np.testing.assert_allclose(mu.eval(), np.zeros((3,)))
+
     x = pm.MvStudentT.dist(nu=4, mu=1, scale=np.eye(3))
     mu = x.owner.inputs[4]
     np.testing.assert_allclose(mu.eval(), np.ones((3,)))