adding logcdf method to inverse gamma distribution

Author: dfm

pymc3/distributions/continuous.py

@@ -2634,6 +2634,39 @@ def logp(self, value):
     def _distr_parameters_for_repr(self):
         return ["alpha", "beta"]
 
+    def logcdf(self, value):
+        """
+        Compute the log of the cumulative distribution function for Inverse Gamma distribution
+        at the specified value.
+
+        Parameters
+        ----------
+        value: numeric
+            Value(s) for which log CDF is calculated. If the log CDF for multiple
+            values are desired the values must be provided in a numpy array or theano tensor.
+
+        Returns
+        -------
+        TensorVariable
+        """
+        alpha = self.alpha
+        beta = self.beta
+        return bound(
+            tt.log(tt.gammaincc(alpha, beta / value)),
+            value >= 0,
+            alpha > 0,
+            beta > 0)
+
+    def _repr_latex_(self, name=None, dist=None):
+        if dist is None:
+            dist = self
+        beta = dist.beta
+        alpha = dist.alpha
+        name = r'\text{%s}' % name
+        return r'${} \sim \text{{InverseGamma}}(\mathit{{alpha}}={},~\mathit{{beta}}={})$'.format(name,
+                                                                get_variable_name(alpha),
+                                                                get_variable_name(beta))
+
 
 class ChiSquared(Gamma):
     r"""

pymc3/tests/test_distributions.py

@@ -920,6 +920,12 @@ def test_inverse_gamma(self):
             {"alpha": Rplus, "beta": Rplus},
             lambda value, alpha, beta: sp.invgamma.logpdf(value, alpha, scale=beta),
         )
+        self.check_logcdf(
+            InverseGamma,
+            Rplus,
+            {'alpha': Rplusbig, 'beta': Rplusbig},
+            lambda value, alpha, beta: sp.invgamma.logcdf(value, alpha, scale=beta),
+        )
 
     @pytest.mark.xfail(
         condition=(theano.config.floatX == "float32"),