Statistical functions (`mlpy.stats`)¶

Discrete distributions¶

`nonuniform`	A non-uniform discrete random variable.
`gibbs`	A Gibbs distribution discrete random variable.

`conditional_normal`	Conditional Normal random variable.
`conditional_student`	Conditional Student random variable.
`conditional_mix_normal`	Conditional Mix-Normal random variable.

`multivariate_normal`	Multivariate Normal random variable.
`multivariate_student`	Multivariate Student random variable.
`invwishart`	Inverse Wishart random variable.
`normal_invwishart`	Normal-Inverse Wishart random variable.

markov Markov model.

`MixtureModel`	Mixture model base class.
`DiscreteMM`	Discrete mixture model class.
`GMM`	Gaussian mixture model class.
`StudentMM`	Student mixture model class.

`is_posdef`	Test if matrix a is positive definite.
`randpd`	Create a random positive definite matrix.
`stacked_randpd`	Create multiple random positive definite matrices.
`normalize_logspace`	Normalize in log space while avoiding numerical underflow.
`sq_distance`	Efficiently compute squared Euclidean distances between stats of vectors.
`partitioned_cov`	Partition the rows of x according to y and take the covariance of each group.
`partitioned_mean`	Groups the rows of x according to the class labels in y and takes the mean of each group.
`partitioned_sum`	Groups the rows of x according to the class labels in y and sums each group.
`shrink_cov`	Ledoit-Wolf optimal shrinkage estimator.
`canonize_labels`	Transform labels to 1:k.