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Exploring 2D Gaussian Distributions with NumPy

A visual introduction to how changes in mean and covariance shape synthetic data distributions.

Posted Updated
By Shawn Kim
1 min read
Exploring 2D Gaussian Distributions with NumPy

Summary

  • This post explores how synthetic 2D datasets generated from multivariate normal distributions change based on different mean vectors and covariance matrices.

  • Using Python libraries like NumPy and Matplotlib, we visualize how these parameters influence the spread, orientation, and clustering of data points.

  • This is a foundational concept in statistical modeling, useful for understanding data distributions in machine learning and signal processing.

Relevant Coursework, Intro to Machine Learning (CSC 480)
ml/ai python
This post is licensed under CC BY 4.0 by the author.