This article explains the concept of probability distributions, describes the Gaussian (normal) distribution with its parameters, demonstrates how to visualize it using Python code, and discusses random variables, independence, and real‑world examples such as atmospheric CO₂ time‑series data.
The article explains how normal (Gaussian) distributions underpin many machine‑learning algorithms, reviewing the central limit theorem, multivariate Gaussian sampling, and key properties such as products, sums, conditional and marginal distributions, linear transformations, and Gaussian‑based Bayesian inference.
The article examines Didi’s “Guess Where You’re Going” feature, describing its product benefits, the contextual data used, and a simple Bayesian Gaussian model that predicts a user’s destination based on time, departure location, and weekday, while also discussing its limitations and potential improvements.
This article explains why Gaussian (normal) distribution sampling is essential, describes the mathematical transformation from a standard normal, and compares several practical algorithms—including inverse transform, Box‑Muller, Marsaglia polar, rejection sampling, and Ziggurat—highlighting their implementation steps and efficiency considerations.
