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

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

tf.contrib.distributions.MultivariateNormalCholesky.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.MultivariateNormalCholesky.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.MultivariateNormalCholesky.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'

class tf.contrib.distributions.MultivariateNormalCholesky The multivariate normal distribution on R^k. This distribution is defined by a 1-D mean mu and a Cholesky factor chol. Providing the Cholesky factor allows for O(k^2) pdf evaluation and sampling, and requires O(k^2) storage.

tf.contrib.distributions.Multinomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Multinomial') Initialize a batch of Multinomial distributions. Args: n: Non-negative floating point tensor with shape broadcastable to [N1,..., Nm] with m >= 0. Defines this as a batch of N1 x ... x Nm different Multinomial distributions. Its components should be equal to integer values. logits: Floating point tensor representing the log-odds of a positive event with shap