This article explains the Central Limit Theorem, shows how a sum of independent identically distributed variables approaches a normal distribution, and demonstrates its practical use through six detailed examples ranging from power supply planning to medical donor matching.
This article explains the difference between continuous and discrete probability distributions, introduces common examples such as Bernoulli, Binomial, Poisson, Uniform, Exponential, and Normal distributions, and describes how these distributions relate to each other.
This article intuitively demonstrates the Central Limit Theorem using uniform and Beta distributions, showing how sample means converge to a normal shape as sample size grows, and provides the formal statistical statement and its significance for inference.
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.
This article explains the normal (Gaussian) distribution, its probability density function, the 3‑sigma principle, confidence intervals, and extends the discussion to the bivariate normal distribution, illustrating concepts with bell‑shaped curves and contour plots.
The article explains the origin of the normal distribution, the central limit theorem, and how boxplots identify anomalies, then describes a Java‑based API that partitions data into five median‑centered levels using same‑period and year‑over‑year ratios to automatically detect and classify abnormal trends in daily metrics.
The article explains a Java epidemic simulation that models virus transmission with normal distribution, demonstrates how parameters like movement intention, hospital bed capacity, response time, and incubation period affect outbreak dynamics, and emphasizes staying home as the most effective protection.
