jax.random.loggamma

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jax.random.loggamma#

jax.random.loggamma(key, a, shape=None, dtype=None, *, out_sharding=None)[source]#

Sample log-gamma random values with given shape and float dtype.

This function is implemented such that the following will hold for a dtype-appropriate tolerance:

np.testing.assert_allclose(jnp.exp(loggamma(*args)), gamma(*args), rtol=rtol)

The benefit of log-gamma is that for samples very close to zero (which occur frequently when a << 1) sampling in log space provides better precision.

Parameters:

  • key (ArrayLike) – a PRNG key used as the random key.

  • a (RealArray) – a float or array of floats broadcast-compatible with shape representing the parameter of the distribution.

  • shape (Shape | None) – optional, a tuple of nonnegative integers specifying the result shape. Must be broadcast-compatible with a. The default (None) produces a result shape equal to a.shape.

  • dtype (DTypeLikeFloat | None) – optional, a float dtype for the returned values (default float64 if jax_enable_x64 is true, otherwise float32).

  • out_sharding (NamedSharding | P | None) – Optional. Specifies how the output array should be sharded across devices in multi-device computation. Can be a NamedSharding, a PartitionSpec (P), or None (default). When specified, the output will be sharded according to the given sharding specification. Primarily used in explicit sharding mode. See the explicit sharding tutorial for more details.

Returns:

A random array with the specified dtype and with shape given by shape if shape is not None, or else by a.shape.

Return type:

Array

See also

gamma : standard gamma sampler.

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