alpha min value

Author: ColtAllen

pymc_extras/distributions/discrete.py

@@ -512,14 +512,13 @@ def logp(value, r, alpha):
         )
 
     def logcdf(value, r, alpha):
-        # TODO: Math may not be correct here
         logcdf = r * (pt.log(value) - pt.log(alpha + value))
 
         return check_parameters(
             logcdf,
             r > 0,
-            alpha > 0,
-            msg="s > 0, alpha > 0",
+            alpha > 0.6181, # alpha must be greater than 0.6181 for convergence
+            msg="r > 0, alpha > 0",
         )
 
     def support_point(rv, size, r, alpha):
@@ -530,10 +529,7 @@ def support_point(rv, size, r, alpha):
         is Gamma(r, 1/alpha), its mean is r/alpha. We then transform this through
         the geometric link function and round to ensure an integer value.
         """
-        # E[lambda] = r/alpha for Gamma(r, 1/alpha)
-        # p = 1 - exp(-lambda) for geometric
-        # E[T] = 1/p for geometric
-        mean = pt.ceil(pt.exp(alpha/r))  # Conservative upper bound
+        mean = pt.ceil(pt.exp(alpha/r))
 
         if not rv_size_is_none(size):
             mean = pt.full(size, mean)

tests/distributions/test_discrete.py

@@ -214,8 +214,8 @@ def test_logp(self):
 class TestGrassiaIIGeometric:
     class TestRandomVariable(BaseTestDistributionRandom):
         pymc_dist = GrassiaIIGeometric
-        pymc_dist_params = {"r": 1.0, "alpha": 2.0}
-        expected_rv_op_params = {"r": 1.0, "alpha": 2.0}
+        pymc_dist_params = {"r": .5, "alpha": 2.0}
+        expected_rv_op_params = {"r": .5, "alpha": 2.0}
         tests_to_run = [
             "check_pymc_params_match_rv_op",
             "check_rv_size",
@@ -289,11 +289,11 @@ def test_sampling_consistency(self):
             trace = pm.sample(chains=1, draws=1000, random_seed=42).posterior
 
         samples = trace["x"].values.flatten()
-        
+
         # Check basic properties of samples
         assert np.all(samples > 0)  # All values should be positive
         assert np.all(samples.astype(int) == samples)  # All values should be integers
-        
+
         # Check mean and variance are reasonable
         # (exact values depend on the parameterization)
         assert 0 < np.mean(samples) < np.inf