tf.contrib.distributions.Dirichlet.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Dirichlet: Note that the input must be a non-negative tensor with dtype dtype and whose shape can be broadcast with self.alpha. For fixed leading dimensions, the last dimension represents counts for the corresponding Dirichlet distribution in self.alpha. x is only legal if it sums up to one. Args: value: float or double Tensor. name: The na

tf.contrib.distributions.Dirichlet.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.Dirichlet.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Dirichlet.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.Dirichlet.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.Dirichlet.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.Dirichlet.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.Dirichlet.mode(name='mode') Mode. Additional documentation from Dirichlet: Note that the mode for the Dirichlet distribution is only defined when alpha > 1. This returns the mode when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.distributions.Dirichlet.mean(name='mean') Mean.

tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or double T