Probability Distribution Monads in C# with Round Numbers

In software development, modeling uncertainty and probabilistic outcomes accurately is a complex challenge. This text-based course guides you through building a functional probability distribution monad in C# that handles weights efficiently and safely. You will learn to use mathematical optimization techniques to keep your calculations clean and reliable. By the end of this course, you will be able to design, write, and optimize monadic structures in C# to model real-world probabilistic systems without risking arithmetic errors. What you'll learn: Understand the core mathematical and functional programming concepts behind monads; Implement a basic probability distribution monad from scratch in C#; Apply Greatest Common Divisor (GCD) and Least Common Multiple (LCM) techniques to normalize weights; Prevent integer overflow issues when combining multiple distributions; Write clean C# code utilizing modern type safety and pattern matching. The course starts with foundational definitions of monads and probability representations, then moves step-by-step through implementing the C# code, and concludes with practical optimization patterns for real-world applications. This course is designed for beginner to intermediate C# developers who want to explore functional programming concepts and mathematical optimizations. No prior experience with monads is required. Start reading to master functional probability modeling in C# today.