tf.contrib.distributions.DirichletMultinomial.alpha Parameter defining this distribution.

tf.contrib.distributions.DirichletMultinomial.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 fo

class tf.contrib.distributions.DirichletMultinomial DirichletMultinomial mixture distribution. This distribution is parameterized by a vector alpha of concentration parameters for k classes and n, the counts per each class..

tf.contrib.distributions.Dirichlet.__init__(alpha, validate_args=False, allow_nan_stats=True, name='Dirichlet') Initialize a batch of Dirichlet distributions. Args: alpha: Positive floating point tensor with shape broadcastable to [N1,..., Nm, k] m >= 0. Defines this as a batch of N1 x ... x Nm different k class Dirichlet distributions. validate_args: Boolean, default False. Whether to assert valid values for parameters alpha and x in prob and log_prob. If False, correct behavior is not g

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

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

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

tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.