Probabilistic Graphical Models: Reasoning and Inference

Making sense of uncertainty in complex systems requires more than simple statistics; it requires a structured way to reason about interconnected variables. This course provides a clear path to understanding how to perform inference—the process of answering queries and making predictions—within the framework of Probabilistic Graphical Models (PGMs). You will transform your understanding of data by learning how to compute probabilities and find the most likely explanations in systems where many variables interact. By the end of this course, you will be able to select and apply the right inference strategies to solve real-world problems in fields ranging from medical diagnosis to automated decision-making. What you'll learn: - Understand the core principles of exact inference in Bayesian and Markov networks - Apply variable elimination and message-passing algorithms to compute marginal probabilities - Practice approximate inference techniques like Markov Chain Monte Carlo (MCMC) for high-dimensional data - Explore variational inference as a modern approach to handling complex posterior distributions - Analyze the computational trade-offs between different inference strategies - Connect graphical models to modern machine learning concepts like latent variables and deep generative models The course begins with foundational definitions of inference tasks and the mathematical logic behind them. You will then progress through structured written explanations of core algorithms, moving from exact calculation methods to modern approximation techniques used in industry today. This course is designed for beginners in probabilistic reasoning who have a basic understanding of probability and want to master the logic behind automated inference. No previous experience with graphical models is required. Start learning how to reason with uncertainty through structured probabilistic models.