tf.contrib.distributions.MultivariateNormalCholesky.mode(name='mode') Mode.

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

tf.contrib.distributions.MultivariateNormalCholesky.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:

tf.contrib.distributions.MultivariateNormalCholesky.log_sigma_det(name='log_sigma_det') Log of determinant of covariance matrix.

tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from _MultivariateNormalOperatorPD: x is a batch vector with compatible shape if x is a Tensor whose shape can be broadcast up to either: self.batch_shape + self.event_shape or [M1,...,Mm] + self.batch_shape + self.event_shape Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob

tf.contrib.distributions.MultivariateNormalCholesky.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.MultivariateNormalCholesky.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.MultivariateNormalCholesky.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 samp

tf.contrib.distributions.MultivariateNormalCholesky.is_reparameterized

tf.contrib.distributions.MultivariateNormalCholesky.is_continuous