Practicing Data-Based Bayesian Networks and Model Evaluation

Understanding how to evaluate probabilistic graphical models is critical for reliable data-driven decision-making. This text-based course helps you master the core principles of Bayesian networks, focusing on how structure, data-based hyperparameters, and evaluation metrics interact. You will learn to assess model performance and tune parameters effectively through written explanations and analytical exercises. What you'll learn: Understand foundational Bayesian network structures and conditional probability tables; Analyze how data-based hyperparameters influence network learning and structure search; Evaluate model performance using key metrics like log-likelihood and information criteria; Identify common overfitting patterns in probabilistic graphical models; Apply diagnostic reasoning to troubleshoot network parameters. This course begins with essential terminology and the mathematical foundations of directed acyclic graphs before moving into advanced evaluation metrics and hyperparameter tuning. It is designed for beginners and intermediate learners who want to solidify their theoretical knowledge of probabilistic models without needing prior advanced programming experience. Start refining your model evaluation skills today.