tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.df Degrees of freedom in these Student's t distribution(s).

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Stud

class tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma StudentT with df = floor(abs(df)) and sigma = softplus(sigma).

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 tens

tf.contrib.distributions.StudentT.variance(name='variance') Variance. Additional documentation from StudentT: The variance for Student's T equals df / (df - 2), when df > 2 infinity, when 1 < df <= 2 NaN, when df <= 1

tf.contrib.distributions.StudentT.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.StudentT.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.StudentT.std(name='std') Standard deviation.