- Add release note

RELEASE-NOTES.md

@@ -31,6 +31,7 @@ It also brings some dreadfully awaited fixes, so be sure to go through the chang
 - Fix issue in `logp` method of `HyperGeometric`. It now returns `-inf` for invalid parameters (see [4367](https://github.com/pymc-devs/pymc3/pull/4367))
 - Fixed `MatrixNormal` random method to work with parameters as random variables. (see [#4368](https://github.com/pymc-devs/pymc3/pull/4368))
 - Update the `logcdf` method of several continuous distributions to return -inf for invalid parameters and values, and raise an informative error when multiple values cannot be evaluated in a single call. (see [4393](https://github.com/pymc-devs/pymc3/pull/4393))
+- Improve numerical stability in `logp` and `logcdf` methods of `ExGaussian` (see [#4407](https://github.com/pymc-devs/pymc3/pull/4407))
 
 ## PyMC3 3.10.0 (7 December 2020)
 

pymc3/tests/test_distributions.py

@@ -546,7 +546,6 @@ def check_logp(self, model, value, domain, paramdomains, logp_reference, decimal
         logp = model.fastlogp
         for pt in product(domains, n_samples=100):
             pt = Point(pt, model=model)
-            print(pt)
             if decimal is None:
                 decimal = select_by_precision(float64=6, float32=3)
             assert_almost_equal(logp(pt), logp_reference(pt), decimal=decimal, err_msg=str(pt))
@@ -566,7 +565,6 @@ def check_logcdf(
             decimal = select_by_precision(float64=6, float32=3)
         for pt in product(domains, n_samples=n_samples):
             params = dict(pt)
-            print(params)
             scipy_cdf = scipy_logcdf(**params)
             value = params.pop("value")
             dist = pymc3_dist.dist(**params)