Markov Processes in C#: Modeling Discrete State Transitions

Understanding how systems transition between states over time is essential for game development, simulation, and predictive modeling. This course guides you through implementing Markov chains and discrete probability distributions from scratch using C#. You will transition from writing basic procedural code to designing structured, state-based simulations. By the end of this text-based course, you will be able to construct transition matrices, sample sequences, and run predictive simulations using modern C# programming techniques. What you'll learn: Understand the foundational concepts of Markov processes, states, and transition probabilities; Implement discrete probability distributions and custom sampling logic in C#; Use modern C# features like record types and pattern matching to represent states and transitions cleanly; Build transition matrices and simulate sequential state changes over time; Analyze and validate simulated sequences through written exercises and structured code reviews. You will start with the mathematical foundations of probability and state transitions before moving into hands-on C# implementation. The material guides you step-by-step through designing state representations, coding transition algorithms, and running multi-step simulations. This course is designed for beginner to intermediate C# developers, software engineers, and simulation enthusiasts who want to learn stochastic modeling. A basic familiarity with C# syntax is recommended, but no prior background in advanced mathematics or probability is required. Start reading today to master state transition modeling with C#.