C# for Probabilistic Workflows: A Streamlined Guide

Modeling uncertainty and working with discrete probability distributions can often feel overly complex in standard programming environments. This text-based course guides you through using a specialized, streamlined subset of C# to write clean, efficient probabilistic workflows. You will transition from writing verbose simulation code to crafting elegant, readable probabilistic models. By focusing on essential language features and custom operators, you will learn how to represent random variables, evaluate outcomes, and structure decision-making processes under uncertainty. What you'll learn: - Understand the core terminology of probabilistic programming and discrete distributions. - Apply a streamlined subset of C# syntax to represent random variables and sample spaces. - Practice building custom operators to combine and manipulate probability distributions. - Design clean workflows that handle uncertainty without complex boilerplate code. - Utilize modern C# features like records and pattern matching to structure model data. - Analyze and evaluate probabilistic models through written structured exercises. The course begins with foundational concepts of probability and C# syntax before moving into custom operators and discrete distribution modeling. You will progress through practical scenarios that demonstrate how to structure, run, and refine your probabilistic workflows. This course is designed for beginners who want to explore probabilistic programming using C#, with no prior experience with advanced statistics required. Start mastering probabilistic workflows in C# today through clear, step-by-step written explanations.