This is a fundamental course to provide the general concepts and applications of probabilistic graphical models(PGMs). PGMs are a marriage between graphical theory and graph theory. They provide a unified tool to not only modeling complex dependencies among random variables using graphs, but also giving a fully probabilistic interpretation of these dependencies.
Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.
Address:
2001 Longxiang Blvd., Longgang District,
Shenzhen, Guangdong, China
Tel: (0755)23515151 (University Library)
Email: library@cuhk.edu.cn
on WeChat
on Bilibili
