tf.contrib.distributions.Gamma.__init__()

tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='Gamma')

Construct Gamma distributions with parameters alpha and beta.

The parameters alpha and beta must be shaped in a way that supports broadcasting (e.g. alpha + beta is a valid operation).

Args:

• alpha: Floating point tensor, the shape params of the distribution(s). alpha must contain only positive values.
• beta: Floating point tensor, the inverse scale params of the distribution(s). beta must contain only positive values.
• validate_args: Boolean, default False. Whether to assert that a > 0, b > 0, and that x > 0 in the methods prob(x) and log_prob(x). If validate_args is False and the inputs are invalid, correct behavior is not guaranteed.
• allow_nan_stats: Boolean, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
• name: The name to prepend to all ops created by this distribution.

Raises:

• TypeError: if alpha and beta are different dtypes.