tf.contrib.distributions.Poisson.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Poisson.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Poisson.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.Poisson.entropy(name='entropy') Shanon entropy in nats.

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

tf.contrib.distributions.Poisson.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.Poisson.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 u

class tf.contrib.distributions.Poisson Poisson distribution. The Poisson distribution is parameterized by lam, the rate parameter. The pmf of this distribution is: pmf(k) = e^(-lam) * lam^k / k!, k >= 0

tf.contrib.distributions.normal_conjugates_known_sigma_posterior(prior, sigma, s, n) Posterior Normal distribution with conjugate prior on the mean. This model assumes that n observations (with sum s) come from a Normal with unknown mean mu (described by the Normal prior) and known variance sigma^2. The "known sigma posterior" is the distribution of the unknown mu. Accepts a prior Normal distribution object, having parameters mu0 and sigma0, as well as known sigma values of the predictive dist