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

tf.contrib.distributions.Gamma.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.Gamma.alpha Shape parameter.

tf.contrib.distributions.Gamma.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 und

class tf.contrib.distributions.Gamma The Gamma distribution with parameter alpha and beta. The parameters are the shape and inverse scale parameters alpha, beta. The PDF of this distribution is: pdf(x) = (beta^alpha)(x^(alpha-1))e^(-x*beta)/Gamma(alpha), x > 0 and the CDF of this distribution is: cdf(x) = GammaInc(alpha, beta * x) / Gamma(alpha), x > 0 where GammaInc is the incomplete lower Gamma function. WARNING: This distribution may draw 0-valued samples for small alpha values. See

tf.contrib.distributions.ExponentialWithSoftplusLam.__init__(lam, validate_args=False, allow_nan_stats=True, name='ExponentialWithSoftplusLam')

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

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

tf.contrib.distributions.ExponentialWithSoftplusLam.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`.