Add Exponentially scaled modified Bessel Op

pytensor/link/jax/dispatch/scalar.py

@@ -27,6 +27,7 @@
     Erfcx,
     Erfinv,
     Iv,
+    Ive,
     Log1mexp,
     Psi,
     TriGamma,
@@ -267,6 +268,13 @@ def iv(v, x):
     return iv
 
 
+@jax_funcify.register(Ive)
+def jax_funcify_Ive(op, **kwargs):
+    ive = try_import_tfp_jax_op(op, jax_op_name="bessel_ive")
+
+    return ive
+
+
 @jax_funcify.register(Log1mexp)
 def jax_funcify_Log1mexp(op, node, **kwargs):
     def log1mexp(x):

pytensor/scalar/math.py

@@ -1197,6 +1197,37 @@ def c_code(self, *args, **kwargs):
 i0 = I0(upgrade_to_float, name="i0")
 
 
+class Ive(BinaryScalarOp):
+    """
+    Exponentially scaled modified Bessel function of the first kind of order v (real).
+    """
+
+    nfunc_spec = ("scipy.special.ive", 2, 1)
+
+    @staticmethod
+    def st_impl(v, x):
+        return scipy.special.ive(v, x)
+
+    def impl(self, v, x):
+        return self.st_impl(v, x)
+
+    def grad(self, inputs, grads):
+        v, x = inputs
+        (gz,) = grads
+        return [
+            grad_not_implemented(self, 0, v),
+            gz
+            * (ive(v - 1, x) - 2.0 * _unsafe_sign(x) * ive(v, x) + ive(v + 1, x))
+            / 2.0,
+        ]
+
+    def c_code(self, *args, **kwargs):
+        raise NotImplementedError()
+
+
+ive = Ive(upgrade_to_float, name="ive")
+
+
 class Sigmoid(UnaryScalarOp):
     """
     Logistic sigmoid function (1 / (1 + exp(-x)), also known as expit or inverse logit

pytensor/tensor/inplace.py

@@ -313,6 +313,11 @@ def iv_inplace(v, x):
     """Modified Bessel function of the first kind of order v (real)."""
 
 
+@scalar_elemwise
+def ive_inplace(v, x):
+    """Exponentially scaled modified Bessel function of the first kind of order v (real)."""
+
+
 @scalar_elemwise
 def sigmoid_inplace(x):
     """Logistic sigmoid function (1 / (1 + exp(-x)), also known as expit or inverse logit"""

pytensor/tensor/math.py

@@ -1435,6 +1435,11 @@ def iv(v, x):
     """Modified Bessel function of the first kind of order v (real)."""
 
 
+@scalar_elemwise
+def ive(v, x):
+    """Exponentially scaled modified Bessel function of the first kind of order v (real)."""
+
+
 @scalar_elemwise
 def sigmoid(x):
     """Logistic sigmoid function (1 / (1 + exp(-x)), also known as expit or inverse logit"""
@@ -3039,6 +3044,7 @@ def vectorize_node_to_matmul(op, node, batched_x, batched_y):
     "i0",
     "i1",
     "iv",
+    "ive",
     "sigmoid",
     "expit",
     "softplus",

tests/tensor/test_math_scipy.py

@@ -75,6 +75,7 @@ def scipy_special_gammal(k, x):
 expected_i0 = scipy.special.i0
 expected_i1 = scipy.special.i1
 expected_iv = scipy.special.iv
+expected_ive = scipy.special.ive
 expected_erfcx = scipy.special.erfcx
 expected_sigmoid = scipy.special.expit
 expected_hyp2f1 = scipy.special.hyp2f1
@@ -639,6 +640,23 @@ def fixed_first_input_jv(x):
     inplace=True,
 )
 
+TestIveBroadcast = makeBroadcastTester(
+    op=at.ive,
+    expected=expected_ive,
+    good=_good_broadcast_binary_bessel,
+    eps=2e-10,
+    mode=mode_no_scipy,
+)
+
+TestIveInplaceBroadcast = makeBroadcastTester(
+    op=inplace.ive_inplace,
+    expected=expected_ive,
+    good=_good_broadcast_binary_bessel,
+    eps=2e-10,
+    mode=mode_no_scipy,
+    inplace=True,
+)
+
 
 def test_verify_iv_grad():
     # Verify Iv gradient.
@@ -652,6 +670,18 @@ def fixed_first_input_iv(x):
     utt.verify_grad(fixed_first_input_iv, [x_val])
 
 
+def test_verify_ive_grad():
+    # Verify Ive gradient.
+    # Implemented separately due to need to fix first input for which grad is
+    # not defined.
+    v_val, x_val = _grad_broadcast_binary_bessel["normal"]
+
+    def fixed_first_input_ive(x):
+        return at.ive(v_val, x)
+
+    utt.verify_grad(fixed_first_input_ive, [x_val])
+
+
 TestSigmoidBroadcast = makeBroadcastTester(
     op=at.sigmoid,
     expected=expected_sigmoid,