Conditional Probabilities and Likelihood Functions in C#

When building simulation engines, game logic, or decision-making algorithms, handling uncertainty accurately is a core requirement. This text-only course guides you through the process of implementing conditional probabilities and likelihood functions using modern C#. You will learn how to translate statistical concepts into clean, maintainable object-oriented code. By completing this course, you will transition from writing simple randomized scripts to developing structured probability models. You will understand how to represent likelihoods, chain conditional events, and build custom discrete distribution samplers using modern C# features. What you'll learn: - Understand the core mathematical foundations of conditional probability and likelihood functions - Implement discrete distribution samplers using modern System.Random enhancements - Structure probability models using C# type hints, records, and pattern matching - Simulate dependent event chains and compute posterior probabilities programmatically - Write unit tests with pytest-style paradigms adapted for C# to verify statistical correctness The course starts with essential probability terminology and foundational mathematical concepts before moving into hands-on C# implementation. You will progress from basic random generation to building modular, testable simulation components. This course is designed for beginner to intermediate C# developers, software engineers, and simulation hobbyists who want to implement statistical models without relying on heavy third-party libraries. No advanced math background is required. Start mastering probability modeling in C# today.