Skip to content


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. Samples that are assigned low probability density are more likely to be outliers.

Interfaces: Estimator, Learner, Online, Scoring, Persistable

Data Type Compatibility: Continuous


# Name Default Type Description
1 contamination 0.1 float The proportion of outliers that are assumed to be present in the training set.
2 smoothing 1e-9 float The amount of epsilon smoothing added to the variance of each feature.


use Rubix\ML\AnomalyDetectors\GaussianMLE;

$estimator = new GaussianMLE(0.03, 1e-8);

Additional Methods#

Return the column means computed from the training set:

public means() : float[]

Return the column variances computed from the training set:

public variances() : float[]


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