| #69035 in Books | Koller Daphne Friedman Nir | 2009-07-31 | Original language:English | PDF # 1 | 9.00 x1.69 x8.00l,4.65 | File type: PDF | 1270 pages | Probabilistic Graphical Models Principles and Techniques||23 of 25 people found the following review helpful.| used for Coursera PGM course|By catwings|I bought this book to use for the Coursera course on PGM taught by the author. It was essential to being able to follow the course. I would not say that it is an easy book to pick up and learn from. It was a good reference to use to get more details on the topics covered in the lectures.|4 of 5 people found the following review helpfu|||This landmark book provides a very extensive coverage of the field, ranging from basic representational issues to the latest techniques for approximate inference and learning. As such, it is likely to become a definitive reference for all those who work in th
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 c...
You can specify the type of files you want, for your gadget.Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) | Daphne Koller, Nir Friedman. A good, fresh read, highly recommended.
