use existing aesara laplace implementation

Author: farhanreynaldo

pymc3/distributions/continuous.py

@@ -34,6 +34,7 @@
     halfcauchy,
     halfnormal,
     invgamma,
+    laplace,
     logistic,
     lognormal,
     normal,
@@ -1469,21 +1470,6 @@ def logcdf(value, mu):
         )
 
 
-class LaplaceRV(RandomVariable):
-    name = "laplace"
-    ndim_supp = 0
-    ndims_params = [0, 0]
-    dtype = "floatX"
-    _print_name = ("Laplace", "\\operatorname{Laplace}")
-
-    @classmethod
-    def rng_fn(cls, rng, mu, b, size=None):
-        return stats.laplace.rvs(mu, b, size=size, random_state=rng)
-
-
-laplace = LaplaceRV()
-
-
 class Laplace(Continuous):
     r"""
     Laplace log-likelihood.
@@ -1534,7 +1520,7 @@ def dist(cls, mu, b, *args, **kwargs):
         mu = at.as_tensor_variable(floatX(mu))
 
         assert_negative_support(b, "b", "Laplace")
-        return super().dist([mu, b], **kwargs)
+        return super().dist([mu, b], *args, **kwargs)
 
     def logp(value, mu, b):
         """

pymc3/tests/test_distributions_random.py

@@ -1123,12 +1123,6 @@ def ref_rand(size, mu, lam, alpha):
             ref_rand=ref_rand,
         )
 
-    def test_laplace(self):
-        def ref_rand(size, mu, b):
-            return st.laplace.rvs(mu, b, size=size)
-
-        pymc3_random(pm.Laplace, {"mu": R, "b": Rplus}, ref_rand=ref_rand)
-
     @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
     def test_laplace_asymmetric(self):
         def ref_rand(size, kappa, b, mu):