Scale used in exponential and logp, logcdf checks not required.

Author: manulpatel

pymc/distributions/continuous.py

@@ -1359,12 +1359,12 @@ def dist(cls, lam=None, scale=None, *args, **kwargs):
         elif lam is None and scale is None:
             raise ValueError("Incompatible parametrization. Must specify either lam or scale.")
 
-        if scale is not None:
-            lam = pt.reciprocal(scale)
+        if scale is None:
+            scale = pt.reciprocal(lam)
 
-        lam = pt.as_tensor_variable(floatX(lam))
+        scale = pt.as_tensor_variable(floatX(scale))
         # PyTensor exponential op is parametrized in terms of mu (1/lam)
-        return super().dist([pt.reciprocal(lam)], **kwargs)
+        return super().dist([scale], **kwargs)
 
     def moment(rv, size, mu):
         if not rv_size_is_none(size):

tests/distributions/test_continuous.py

@@ -432,24 +432,12 @@ def test_exponential(self):
             {"lam": Rplus},
             lambda value, lam: st.expon.logpdf(value, 0, 1 / lam),
         )
-        check_logp(
-            pm.Exponential,
-            Rplus,
-            {"scale": Rplus},
-            lambda value, scale: st.expon.logpdf(value, 0, scale),
-        )
         check_logcdf(
             pm.Exponential,
             Rplus,
             {"lam": Rplus},
             lambda value, lam: st.expon.logcdf(value, 0, 1 / lam),
         )
-        check_logcdf(
-            pm.Exponential,
-            Rplus,
-            {"scale": Rplus},
-            lambda value, scale: st.expon.logcdf(value, 0, scale),
-        )
         check_icdf(
             pm.Exponential,
             {"lam": Rplus},