tf.contrib.distributions.Mixture.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.Mixture.entropy_lower_bound(name='entropy_lower_bound') A lower bound on the entropy of this mixture model. The bound below is not always very tight, and its usefulness depends on the mixture probabilities and the components in use. A lower bound is useful for ELBO when the Mixture is the variational distribution: \( \log p(x) >= ELBO = \int q(z) \log p(x, z) dz + H[q] \) where \( p \) is the prior disribution, \( q \) is the variational, and \( H[q] \) is the entro

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

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

tf.contrib.distributions.Mixture.components

tf.contrib.distributions.Mixture.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.Mixture.cat

tf.contrib.distributions.Mixture.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.Mixture.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.Mixture Mixture distribution. The Mixture object implements batched mixture distributions. The mixture model is defined by a Categorical distribution (the mixture) and a python list of Distribution objects. Methods supported include log_prob, prob, mean, sample, and entropy_lower_bound.