tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from QuantizedDistribution: For whole numbers y, P[Y = y] := P[X <= lower_cutoff], if y == lower_cutoff, := P[X > upper_cutoff - 1], y == upper_cutoff, := 0, if j < lower_cutoff or y > upper_cutoff, := P[y - 1 < X <= y], all other y. The base distribution's log_cdf method mus

tf.contrib.distributions.QuantizedDistribution.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.QuantizedDistribution.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.QuantizedDistribution.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. Additional documentation from QuantizedDistribution: For whole numbers y, cdf(y) := P[Y <= y] = 1, if y >

tf.contrib.distributions.QuantizedDistribution.is_reparameterized

tf.contrib.distributions.QuantizedDistribution.is_continuous

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