tf.contrib.distributions.Poisson.mode(name='mode') Mode. Additional documentation from Poisson: Note that when lam is an integer, there are actually two modes. Namely, lam and lam - 1 are both modes. Here we return only the larger of the two modes.

tf.contrib.distributions.Poisson.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 Ten

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

tf.contrib.distributions.Poisson.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from Poisson: Note thet the input value must be a non-negative floating point tensor with dtype dtype and whose shape can be broadcast with self.lam. x is only legal if it is non-negative and its components are equal to integer values. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor o

tf.contrib.distributions.Poisson.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.

tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_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.Poisson.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_shape(x) + self.

tf.contrib.distributions.Poisson.lam Rate parameter.

tf.contrib.distributions.Poisson.is_reparameterized

tf.contrib.distributions.Poisson.is_continuous