tf.contrib.distributions.StudentT.__init__()

tf.contrib.distributions.StudentT.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentT')

Construct Student's t distributions.

The distributions have degree of freedom df, mean mu, and scale sigma.

The parameters df, mu, and sigma must be shaped in a way that supports broadcasting (e.g. df + mu + sigma is a valid operation).

Args:

• df: Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values.
• mu: Floating point tensor, the means of the distribution(s).
• sigma: Floating point tensor, the scaling factor for the distribution(s). sigma must contain only positive values. Note that sigma is not the standard deviation of this distribution.
• validate_args: Boolean, default False. Whether to assert that df > 0 and sigma > 0. If validate_args is False and 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 give Ops created by the initializer.

Raises:

• TypeError: if mu and sigma are different dtypes.