Non-Uniform Discrete Distributions in C#

Generating random outcomes with unequal probabilities is a fundamental challenge in game development, simulations, and randomized algorithms. This text-based course guides you through the essential mathematics and implementation strategies to generate non-uniform discrete distributions efficiently. You will start with the core concepts of probability mass functions before writing clean, optimized code to handle weighted outcomes. By the end of this course, you will understand how to transition from simple, slow sampling techniques to high-performance, constant-time algorithms. What you will learn: Understand the foundational theory of discrete probability and weighted distribution sampling; Implement linear and binary search cumulative distribution function (CDF) sampling; Master the rejection sampling method and identify its performance bottlenecks; Build the alias method for constant-time, O(1) sampling of complex distributions; Apply modern C# features, including span types and system random enhancements, for memory-efficient execution. The course begins with foundational definitions and probability basics, then moves step-by-step through increasingly optimized algorithms with clear code walkthroughs. This course is designed for intermediate programmers and confident beginners who want to deepen their understanding of randomized algorithms in C# without any complex prerequisites. Start optimizing your simulation and game logic with efficient sampling techniques today.