This post provides a foundational overview of sorting algorithms, specifically focusing on Bubble Sort. It covers the problem definition, key properties like stability, and the practical aspects of sorting. For Bubble Sort, the article details its mechanics through an animated illustration, provides a comprehensive complexity analysis (showing its $O(n^2)$ time complexity), and includes a sample Java implementation with explained logic.

This post demonstrates how to apply Bayes' Theorem, a fundamental concept in probability theory, to estimate the true number of COVID-19 infections based on early, fragmentary testing data. It breaks down the mathematical model, utilizes official population and early testing statistics from the US, introduces crucial conditional probabilities, and discusses how asymptomatic cases or low testing rates (represented by a variable) significantly impact the accuracy of official infection counts. The approach showcases practical data interpretation under uncertainty.