Bayesian Network Parameter Learning with CausalNex and Python

Probabilistic graphical models are essential for reasoning under uncertainty, but building them requires learning their parameters accurately from real-world data. This text-based course guides you through the foundational math and practical Python implementations needed to estimate these parameters with confidence. You will transition from understanding basic probability concepts to implementing robust parameter estimation algorithms. By working through clear written explanations and practical code examples, you will learn how to apply Maximum Likelihood Estimation and Bayesian estimation techniques to reconstruct the conditional probability distributions that power Bayesian networks. What you'll learn: - Understand foundational probability concepts, conditional independence, and the structure of Bayesian networks. - Apply Maximum Likelihood Estimation to calculate network parameters directly from observational data. - Implement Bayesian estimation to incorporate prior knowledge and handle sparse data scenarios effectively. - Configure and use the CausalNex library in Python to define structures and learn parameters. - Evaluate model performance using log-likelihood, cross-validation, and validation metrics. - Practice clean coding standards by writing type-hinted Python code for reproducible data workflows. The course begins with foundational definitions of probability and network architecture before advancing to step-by-step code implementations of estimation algorithms. You will progress through structured text explanations, code walkthroughs, and conceptual exercises designed to solidify your understanding of parameter learning. This course is designed for data analysts, aspiring data scientists, and developers who are new to probabilistic graphical models. No prior experience with Bayesian networks is required, though a basic familiarity with Python is helpful. Start reading today to unlock the power of probabilistic reasoning and parameter estimation in your data projects.