Gaussian MLE#

The Gaussian Maximum Likelihood Estimator (MLE) is able to spot outliers by computing a probability density function (PDF) over the features assuming they are independently and normally (Gaussian) distributed.

Interfaces: Estimator, Learner, Online, Ranking, Persistable

Data Type Compatibility: Continuous


# Param Default Type Description
1 contamination 0.1 float The proportion of outliers that are assumed to be present in the training set.

Additional Methods#

Return the column means computed from the training set:

public means() : array

Return the column variances computed from the training set:

public variances() : array


use Rubix\ML\AnomalyDetectors\GaussianMLE;

$estimator = new GaussianMLE(0.03);


  • T. F. Chan et al. (1979). Updating Formulae and a Pairwise Algorithm for Computing Sample Variances.