Add support for discrete rvs

pymc/logprob/order.py

@@ -45,6 +45,8 @@
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.var import TensorVariable
 
+import pymc as pm
+
 from pymc.logprob.abstract import (
     MeasurableVariable,
     _logcdf_helper,
@@ -63,7 +65,7 @@ class MeasurableMax(Max):
 
 
 class MeasurableMaxDiscrete(Max):
-    """A placeholder used to specify a log-likelihood for a cmax sub-graph."""
+    """A placeholder used to specify a log-likelihood for sub-graphs of maxima of discrete variables"""
 
 
 MeasurableVariable.register(MeasurableMaxDiscrete)
@@ -101,14 +103,14 @@ def find_measurable_max(fgraph: FunctionGraph, node: Node) -> Optional[List[Tens
     if axis != base_var_dims:
         return None
 
-    # logprob for discrete distribution
-    if isinstance(base_var.owner.op, RandomVariable) and base_var.owner.op.dtype.startswith("int"):
-        measurable_max = MeasurableMaxDiscrete(list(axis))
-        max_rv_node = measurable_max.make_node(base_var)
-        max_rv = max_rv_node.outputs
+    # distinguish measurable discrete and continuous (because logprob is different)
+    if base_var.owner.op.dtype.startswith("int"):
+        if isinstance(base_var.owner.op, RandomVariable):
+            measurable_max = MeasurableMaxDiscrete(list(axis))
+            max_rv_node = measurable_max.make_node(base_var)
+            max_rv = max_rv_node.outputs
 
-        return max_rv
-    # logprob for continuous distribution
+            return max_rv
     else:
         measurable_max = MeasurableMax(list(axis))
         max_rv_node = measurable_max.make_node(base_var)
@@ -145,16 +147,16 @@ def max_logprob_discrete(op, values, base_rv, **kwargs):
     r"""Compute the log-likelihood graph for the `Max` operation.
 
     The formula that we use here is :
-        \ln(f_{(n)}(x)) = \ln(F(x)^n - F(x-1)^n)
-    where f(x) represents the p.d.f and F(x) represents the c.d.f of the distrivution respectively.
+    .. math::
+        \ln(P_{(n)}(x)) = \ln(F(x)^n - F(x-1)^n)
+    where $P_{(n)}(x)$ represents the p.m.f of the maximum statistic and $F(x)$ represents the c.d.f of the i.i.d. variables.
     """
     (value,) = values
-    logprob = _logprob_helper(base_rv, value)
     logcdf = _logcdf_helper(base_rv, value)
     logcdf_prev = _logcdf_helper(base_rv, value - 1)
 
-    n = base_rv.size
+    [n] = constant_fold([base_rv.size])
 
-    logprob = pt.log((pt.exp(logcdf)) ** n - (pt.exp(logcdf_prev)) ** n)
+    logprob = pm.math.logdiffexp(n * logcdf, n * logcdf_prev)
 
     return logprob

tests/logprob/test_order.py

@@ -39,6 +39,7 @@
 import numpy as np
 import pytensor.tensor as pt
 import pytest
+import scipy.stats as sp
 
 import pymc as pm
 
@@ -149,23 +150,24 @@ def test_max_logprob(shape, value, axis):
     )
 
 
-def test_max_discrete():
-    x = pm.DiscreteUniform.dist(0, 1, size=(3,))
-    x.name = "x"
-    x_max = pt.max(x, axis=-1)
+@pytest.mark.parametrize(
+    "mu, size, value, axis",
+    [(2, 3, 0.85, -1), (2, 3, 0.01, 0), (1, 2, 0.2, None), (0, 4, 0, 0)],
+)
+def test_max_discrete(mu, size, value, axis):
+    x = pm.Poisson.dist(name="x", mu=mu, size=(size))
+    x_max = pt.max(x, axis=axis)
     x_max_value = pt.scalar("x_max_value")
     x_max_logprob = logp(x_max, x_max_value)
 
-    discrete_logprob = _logprob_helper(x, x_max_value)
-    discrete_logcdf = _logcdf_helper(x, x_max_value)
-    discrete_logcdf_prev = _logcdf_helper(x, x_max_value - 1)
-    n = x.size
-    discrete_logprob = pt.log((pt.exp(discrete_logcdf)) ** n - (pt.exp(discrete_logcdf_prev)) ** n)
+    test_value = value
 
-    test_value = 0.85
+    n = size
+    exp_rv = np.exp(sp.poisson(mu).logcdf(test_value)) ** n
+    exp_rv_prev = np.exp(sp.poisson(mu).logcdf(test_value - 1)) ** n
 
     np.testing.assert_allclose(
-        discrete_logprob.eval({x_max_value: test_value}),
+        np.log(exp_rv - exp_rv_prev),
         (x_max_logprob.eval({x_max_value: test_value})),
         rtol=1e-06,
     )