tf.contrib.distributions.Uniform.b

tf.contrib.distributions.Uniform.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 u

tf.contrib.distributions.Uniform.a

class tf.contrib.distributions.Uniform Uniform distribution with a and b parameters. The PDF of this distribution is constant between [a, b], and 0 elsewhere.

tf.contrib.distributions.TransformedDistribution.__init__(base_dist_cls, transform, inverse, log_det_jacobian, name='TransformedDistribution', **base_dist_args) Construct a Transformed Distribution. Args: base_dist_cls: the base distribution class to transform. Must be a subclass of Distribution. transform: a callable that takes a Tensor sample from base_dist and returns a Tensor of the same shape and type. x => y. inverse: a callable that computes the inverse of transform. y => x. If

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

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

tf.contrib.distributions.TransformedDistribution.transform Function transforming x => y.

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