Unlock the power of probabilistic graphical models with this comprehensive guide from The Bookish Owl. This essential resource delves into a general framework for building and utilizing probabilistic models of complex systems, enabling computers to make informed decisions using available data. Discover how these model-based approaches facilitate interpretable models and robust reasoning algorithms. Learn to automatically learn models from data, tackling complex scenarios where manual construction is challenging. With a focus on uncertainty, this book covers Bayesian networks, Markov networks, and continuous models, exploring representation, inference, and learning. Ideal for students and professionals, it provides a deep dive into decision-making under uncertainty and causal reasoning, featuring practical case studies and advanced techniques.