Compiling Probabilistic Workflows to Modern C#

Modeling uncertainty and probabilistic workflows often requires complex domain-specific languages, but running these models efficiently means compiling or lowering them to standard code. This text-based course guides you through the process of translating probabilistic workflows into clean, idiomatic, and modern C# code. You will learn to represent complex discrete distributions and conditional logic without relying on heavy external runtimes. What you'll learn: - Understand the foundational concepts of probabilistic programming and discrete probability distributions. - Represent random variables, conditional probabilities, and sampling strategies using standard C# types. - Lower custom probabilistic domain-specific languages into structured, executable C# code. - Apply modern C# features like pattern matching and records to model complex decision trees and conditional logic. - Implement efficient sampling algorithms and Monte Carlo simulations directly in standard C#. - Optimize generated code for performance and memory efficiency using modern language patterns. The course begins with foundational definitions of probability modeling and the mechanics of code generation. You will then progress through clear, written explanations and structured code snippets showing how to parse, lower, and execute these workflows as native, high-performance C# code. This course is designed for C# developers, software architects, and engineers who want to implement probabilistic models directly within their application codebases. No prior background in compiler design is required. Start reading today to master the art of compiling probabilistic models into standard C#.