tf.contrib.distributions.LaplaceWithSoftplusScale.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.LaplaceWithSoftplusScale.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.LaplaceWithSoftplusScale.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.LaplaceWithSoftplusScale.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

class tf.contrib.distributions.LaplaceWithSoftplusScale Laplace with softplus applied to scale.

tf.contrib.distributions.Laplace.__init__(loc, scale, validate_args=False, allow_nan_stats=True, name='Laplace') Construct Laplace distribution with parameters loc and scale. The parameters loc and scale must be shaped in a way that supports broadcasting (e.g., loc / scale is a valid operation). Args: loc: Floating point tensor which characterizes the location (center) of the distribution. scale: Positive floating point tensor which characterizes the spread of the distribution. validate_arg

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

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

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