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

tf.contrib.distributions.Dirichlet.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.Dirichlet.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.Dirichlet.alpha_sum Sum of shape parameter.

tf.contrib.distributions.Dirichlet.alpha Shape parameter.

tf.contrib.distributions.Dirichlet.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

class tf.contrib.distributions.Dirichlet Dirichlet distribution. This distribution is parameterized by a vector alpha of concentration parameters for k classes.

tf.contrib.distributions.Chi2WithAbsDf.__init__(df, validate_args=False, allow_nan_stats=True, name='Chi2WithAbsDf')

tf.contrib.distributions.Chi2WithAbsDf.variance(name='variance') Variance.

tf.contrib.distributions.Chi2WithAbsDf.validate_args Python boolean indicated possibly expensive checks are enabled.