Implement ChiSquare via Gamma

doc/library/tensor/random/basic.rst

@@ -82,9 +82,6 @@ PyTensor can produce :class:`RandomVariable`\s that draw samples from many diffe
 .. autoclass:: pytensor.tensor.random.basic.CategoricalRV
    :members: __call__
 
-.. autoclass:: pytensor.tensor.random.basic.ChiSquareRV
-   :members: __call__
-
 .. autoclass:: pytensor.tensor.random.basic.DirichletRV
    :members: __call__
 

pytensor/link/numba/dispatch/random.py

@@ -195,7 +195,6 @@ def {sized_fn_name}({random_fn_input_names}):
 @numba_funcify.register(aer.NormalRV)
 @numba_funcify.register(aer.LogNormalRV)
 @numba_funcify.register(aer.GammaRV)
-@numba_funcify.register(aer.ChiSquareRV)
 @numba_funcify.register(aer.ParetoRV)
 @numba_funcify.register(aer.GumbelRV)
 @numba_funcify.register(aer.ExponentialRV)

pytensor/tensor/random/basic.py

@@ -487,56 +487,8 @@ def gamma(shape, rate=None, scale=None, **kwargs):
     return _gamma(shape, scale, **kwargs)
 
 
-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.
-
-        Signature
-        ---------
-
-        `() -> ()`
-
-        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()
+def chisquare(df, **kwargs):
+    return gamma(shape=df / 2.0, scale=2.0, **kwargs)
 
 
 class ParetoRV(ScipyRandomVariable):

pytensor/tensor/random/rewriting/jax.py

@@ -7,7 +7,6 @@
 from pytensor.tensor.elemwise import DimShuffle
 from pytensor.tensor.random.basic import (
     BetaBinomialRV,
-    ChiSquareRV,
     GenGammaRV,
     GeometricRV,
     HalfNormalRV,
@@ -104,13 +103,6 @@ def inverse_gamma_from_gamma(fgraph, node):
     return [next_rng, reciprocal(g)]
 
 
-@node_rewriter([ChiSquareRV])
-def chi_square_from_gamma(fgraph, node):
-    *other_inputs, df = node.inputs
-    next_rng, g = _gamma.make_node(*other_inputs, df / 2, 2).outputs
-    return [next_rng, g]
-
-
 @node_rewriter([GenGammaRV])
 def generalized_gamma_from_gamma(fgraph, node):
     *other_inputs, alpha, p, lambd = node.inputs
@@ -171,11 +163,6 @@ def beta_binomial_from_beta_binomial(fgraph, node):
     in2out(inverse_gamma_from_gamma),
     "jax",
 )
-random_vars_opt.register(
-    "chi_square_from_gamma",
-    in2out(chi_square_from_gamma),
-    "jax",
-)
 random_vars_opt.register(
     "generalized_gamma_from_gamma",
     in2out(generalized_gamma_from_gamma),