Adding independent Gaussian noise to any random variable—no matter its original form—ensures the result has a well-defined probability density. In machine learning, this principle is used in generative probabilistic modeling trained by MLE to ensure a well-defined objective. The note below explores the math behind this phenomenon and its practical implications: pdf.