Implement Mixture logcdf

pymc/distributions/mixture.py

@@ -18,7 +18,7 @@
 import numpy as np
 
 from aeppl.abstract import MeasurableVariable, _get_measurable_outputs
-from aeppl.logprob import _logprob
+from aeppl.logprob import _logcdf, _logprob
 from aesara.compile.builders import OpFromGraph
 from aesara.tensor import TensorVariable
 from aesara.tensor.random.op import RandomVariable
@@ -33,7 +33,7 @@
     _get_moment,
     get_moment,
 )
-from pymc.distributions.logprob import logp
+from pymc.distributions.logprob import logcdf, logp
 from pymc.distributions.shape_utils import to_tuple
 from pymc.util import check_dist_not_registered
 
@@ -404,6 +404,38 @@ def marginal_mixture_logprob(op, values, rng, weights, *components, **kwargs):
     return mix_logp
 
 
+@_logcdf.register(MarginalMixtureRV)
+def marginal_mixture_logcdf(op, value, rng, weights, *components, **kwargs):
+
+    # single component
+    if len(components) == 1:
+        # Need to broadcast value across mixture axis
+        mix_axis = -components[0].owner.op.ndim_supp - 1
+        components_logcdf = logcdf(components[0], at.expand_dims(value, mix_axis))
+    else:
+        components_logcdf = at.stack(
+            [logcdf(component, value) for component in components],
+            axis=-1,
+        )
+
+    mix_logcdf = at.logsumexp(at.log(weights) + components_logcdf, axis=-1)
+
+    # Squeeze stack dimension
+    # There is a Aeasara bug in squeeze with negative axis
+    # mix_logp = at.squeeze(mix_logp, axis=-1)
+    mix_logcdf = at.squeeze(mix_logcdf, axis=mix_logcdf.ndim - 1)
+
+    mix_logcdf = check_parameters(
+        mix_logcdf,
+        0 <= weights,
+        weights <= 1,
+        at.isclose(at.sum(weights, axis=-1), 1),
+        msg="0 <= weights <= 1, sum(weights) == 1",
+    )
+
+    return mix_logcdf
+
+
 @_get_moment.register(MarginalMixtureRV)
 def get_moment_marginal_mixture(op, rv, rng, weights, *components):
     ndim_supp = components[0].owner.op.ndim_supp