feat: Levy distribution

docs/source/distributions.rst

@@ -256,6 +256,14 @@ Laplace
     :show-inheritance:
     :member-order: bysource
 
+Levy
+^^^^
+.. autoclass:: numpyro.distributions.continuous.Levy
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :member-order: bysource
+
 LKJ
 ^^^
 .. autoclass:: numpyro.distributions.continuous.LKJ

numpyro/distributions/__init__.py

@@ -32,6 +32,7 @@
     InverseGamma,
     Kumaraswamy,
     Laplace,
+    Levy,
     LKJCholesky,
     Logistic,
     LogNormal,
@@ -160,6 +161,7 @@
     "Kumaraswamy",
     "Laplace",
     "LeftTruncatedDistribution",
+    "Levy",
     "LKJ",
     "LKJCholesky",
     "Logistic",

numpyro/distributions/continuous.py

@@ -49,6 +49,7 @@
     xlogy,
 )
 from jax.scipy.stats import norm as jax_norm
+from jax.typing import ArrayLike
 
 from numpyro.distributions import constraints
 from numpyro.distributions.discrete import _to_logits_bernoulli
@@ -2966,3 +2967,101 @@ def infer_shapes(
                 batch_shape = lax.broadcast_shapes(concentration, matrix[:-2])
                 event_shape = matrix[-2:]
                 return batch_shape, event_shape
+
+
+class Levy(Distribution):
+    r"""Lévy distribution is a special case of Lévy alpha-stable distribution.
+    Its probability density function is given by,
+
+    .. math::
+        f(x\mid \mu, c) = \sqrt{\frac{c}{2\pi(x-\mu)^{3}}} \exp\left(-\frac{c}{2(x-\mu)}\right), \qquad x > \mu
+
+    where :math:`\mu` is the location parameter and :math:`c` is the scale parameter.
+
+    :param loc: Location parameter.
+    :param scale: Scale parameter.
+    """
+
+    arg_constraints = {
+        "loc": constraints.positive,
+        "scale": constraints.positive,
+    }
+
+    def __init__(self, loc, scale, *, validate_args=None):
+        self.loc, self.scale = promote_shapes(loc, scale)
+        batch_shape = lax.broadcast_shapes(jnp.shape(loc), jnp.shape(scale))
+        self._support = constraints.greater_than(loc)
+        super(Levy, self).__init__(batch_shape, validate_args=validate_args)
+
+    @constraints.dependent_property(is_discrete=False)
+    def support(self):
+        return self._support
+
+    @validate_sample
+    def log_prob(self, value):
+        r"""Compute the log probability density function of the Lévy distribution.
+
+        .. math::
+            \log f(x\mid \mu, c) = \frac{1}{2}\log\left(\frac{c}{2\pi}\right) - \frac{c}{2(x-\mu)}
+            - \frac{3}{2}\log(x-\mu), \qquad x > \mu
+
+        :param value: A batch of samples from the distribution.
+        :return: an array with shape `value.shape[:-self.event_shape]`
+        :rtype: numpy.ndarray
+        """
+        shifted_value = value - self.loc
+        return -0.5 * (
+            jnp.log(2.0 * jnp.pi) - jnp.log(self.scale) + self.scale / shifted_value
+        ) - 1.5 * jnp.log(shifted_value)
+
+    def sample(self, key: ArrayLike, sample_shape: tuple[int, ...] = ()) -> ArrayLike:
+        assert is_prng_key(key)
+        u = random.uniform(key, shape=sample_shape + self.batch_shape)
+        return self.icdf(u)
+
+    def icdf(self, q: ArrayLike) -> ArrayLike:
+        r"""
+        The inverse cumulative distribution function of Lévy distribution is given by,
+
+        .. math::
+            F^{-1}(q\mid \mu, c) = \mu + c\left(\Phi^{-1}(1-q/2)\right)^{-2}
+
+        where :math:`\Phi^{-1}` is the inverse of the standard normal cumulative distribution function.
+
+        :param q: quantile values, should belong to [0, 1].
+        :return: the samples whose cdf values equals to `q`.
+        """
+        return self.loc + self.scale * jnp.power(ndtri(1 - 0.5 * q), -2)
+
+    def cdf(self, value: ArrayLike) -> ArrayLike:
+        r"""The cumulative distribution function of Lévy distribution is given by,
+
+        .. math::
+            F(x\mid \mu, c) = 2 - 2\Phi\left(\sqrt{\frac{c}{x-\mu}}\right)
+
+        where :math:`\Phi` is the standard normal cumulative distribution function.
+
+        :param value: samples from Lévy distribution.
+        :return: output of the cumulative distribution function evaluated at `value`.
+        """
+        inv_standardized = self.scale / (value - self.loc)
+        return 2.0 - 2.0 * ndtr(jnp.sqrt(inv_standardized))
+
+    @property
+    def mean(self) -> ArrayLike:
+        return jnp.broadcast_to(jnp.inf, self.batch_shape)
+
+    @property
+    def variance(self) -> ArrayLike:
+        return jnp.broadcast_to(jnp.inf, self.batch_shape)
+
+    def entropy(self) -> ArrayLike:
+        r"""If :math:`X \sim \text{Levy}(\mu, c)`, then the entropy of :math:`X` is given by,
+
+        .. math::
+            H(X) = \frac{1}{2}+\frac{3}{2}\gamma+\frac{1}{2}\ln{\left(16\pi c^2\right)}
+
+        """
+        return jnp.broadcast_to(
+            0.5 + 1.5 * jnp.euler_gamma + 0.5 * jnp.log(16 * jnp.pi), self.batch_shape
+        ) + jnp.log(self.scale)

test/test_distributions.py

@@ -456,6 +456,7 @@ def __init__(
     ),
     dist.Wishart: _wishart_to_scipy,
     _TruncatedNormal: _truncnorm_to_scipy,
+    dist.Levy: lambda loc, scale: osp.levy(loc=loc, scale=scale),
 }
 
 
@@ -933,6 +934,9 @@ def get_sp_dist(jax_dist):
     T(dist.DoublyTruncatedPowerLaw, np.pi, 5.0, 50.0),
     T(dist.DoublyTruncatedPowerLaw, -1.0, 5.0, 50.0),
     T(dist.DoublyTruncatedPowerLaw, np.pi, 1.0, 2.0),
+    T(dist.Levy, 0.0, 1.0),
+    T(dist.Levy, 0.0, np.array([1.0, 2.0, 10.0])),
+    T(dist.Levy, np.array([1.0, 2.0, 10.0]), np.pi),
 ]
 
 DIRECTIONAL = [