tf.contrib.distributions.Beta.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.Beta.b Shape parameter.

tf.contrib.distributions.Beta.a_b_sum Sum of parameters.

tf.contrib.distributions.Beta.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 Student's T for df = 1 is unde

tf.contrib.distributions.Beta.a Shape parameter.

class tf.contrib.distributions.Beta Beta distribution. This distribution is parameterized by a and b which are shape parameters.

tf.contrib.distributions.BernoulliWithSigmoidP.variance(name='variance') Variance.

tf.contrib.distributions.BernoulliWithSigmoidP.__init__(p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidP')

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

tf.contrib.distributions.BernoulliWithSigmoidP.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`.