tf.contrib.distributions.Exponential.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.Exponential.beta Inverse scale parameter.

tf.contrib.distributions.Exponential.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.Exponential.alpha Shape parameter.

tf.contrib.distributions.Exponential.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

class tf.contrib.distributions.Exponential The Exponential distribution with rate parameter lam. The PDF of this distribution is: prob(x) = (lam * e^(-lam * x)), x > 0 Note that the Exponential distribution is a special case of the Gamma distribution, with Exponential(lam) = Gamma(1, lam).

tf.contrib.distributions.Distribution.__init__(dtype, parameters, is_continuous, is_reparameterized, validate_args, allow_nan_stats, name=None) Constructs the Distribution. This is a private method for subclass use. Args: dtype: The type of the event samples. None implies no type-enforcement. parameters: Python dictionary of parameters used by this Distribution. is_continuous: Python boolean. If True this Distribution is continuous over its supported domain. is_reparameterized: Python bool

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

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

tf.contrib.distributions.Distribution.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`.