Add docstring for `ChiSquareRV`

aesara/tensor/random/basic.py

@@ -432,12 +432,48 @@ gamma = GammaRV()
 
 
 class ChiSquareRV(RandomVariable):
+    r"""A chi square continuous random variable.
+
+    The probability density function for `chisquare` in terms of the number of degrees of
+    freedom :math:`k` is:
+
+    .. math::
+
+        f(x; k) = \frac{(1/2)^{k/2}}{\Gamma(k/2)} x^{k/2-1} e^{-x/2}
+
+    for :math:`k > 2`. :math:`\Gamma` is the gamma function:
+
+    .. math::
+
+        \Gamma(x) = \int_0^{\infty} t^{x-1} e^{-t} \mathrm{d}t
+
+
+    This variable is obtained by summing the squares :math:`k` independent, standard normally
+    distributed random variables.
+
+    """
     name = "chisquare"
     ndim_supp = 0
     ndims_params = [0]
     dtype = "floatX"
     _print_name = ("ChiSquare", "\\operatorname{ChiSquare}")
 
+    def __call__(self, df, size=None, **kwargs):
+        r"""Draw samples from a chisquare distribution.
+
+        Parameters
+        ----------
+        df
+            The number :math:`k` of degrees of freedom. Must be positive.
+        size
+            Sample shape. If the given size is, e.g. `(m, n, k)` then `m * n * k`
+            independent, identically distributed random variables are
+            returned. Default is `None` in which case a single random variable
+            is returned.
+
+        """
+        return super().__call__(df, size=size, **kwargs)
+
 
 chisquare = ChiSquareRV()
 

doc/library/tensor/random/basic.rst

@@ -61,6 +61,9 @@ Aesara can produce :class:`RandomVariable`\s that draw samples from many differe
 .. autoclass:: aesara.tensor.random.basic.GammaRV
    :members: __call__
 
+.. autoclass:: aesara.tensor.random.basic.ChiSquareRV
+   :members: __call__
+
 .. autoclass:: aesara.tensor.random.basic.NormalRV
    :members: __call__