Weighted Sampling and Non-Uniform Discrete Distributions in C#

Randomness in software is rarely purely uniform. Whether you are building game mechanics, simulation models, or randomized algorithms, you often need to select items with different probabilities. This text-only course guides you through the concepts and practical implementation of non-uniform discrete distributions in C#. You will transition from basic random number generation to implementing highly optimized, custom sampling algorithms. By reading and analyzing clean C# code examples, you will learn how to design, test, and optimize probability-based systems. What you'll learn: - Understand the core mathematical concepts of probability mass functions and cumulative distribution functions. - Implement linear scan weighted sampling algorithms for simple, dynamic datasets. - Optimize search times using cumulative distribution arrays paired with binary search algorithms. - Apply modern C# features, including type-safe generics and Span for memory-efficient array operations. - Verify the statistical accuracy of your samplers using basic unit testing and frequency analysis. The course starts with essential probability definitions and foundational C# random APIs before moving step-by-step into advanced algorithmic optimizations. Written for intermediate programmers and game developers looking to expand their algorithmic toolkit, this course requires no previous advanced math background. Start writing efficient, predictable, and robust randomized code today.