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

tf.contrib.distributions.Multinomial.p Event probabilities.

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

tf.contrib.distributions.Multinomial.n Number of trials.

tf.contrib.distributions.Multinomial.mode(name='mode') Mode.

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

tf.contrib.distributions.Multinomial.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

tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from Multinomial: For each batch of counts [n_1,...,n_k], P[counts] is the probability that after sampling n draws from this Multinomial distribution, the number of draws falling in class j is n_j. Note that different sequences of draws can result in the same counts, thus the probability includes a combinatorial coefficient. Note tha

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