| #73454 in Books | imusti | 2009-07-31 | Original language:English | PDF # 1 | 9.00 x1.70 x8.00l,4.65 | File type: PDF | 1280 pages | MIT Press MA||16 of 17 people found the following review helpful.| A useful, comprehensive reference book; awkward to read|By ZZ|This popular book makes a noble attempt at unifying the many different types of probabilistic models used in artificial intelligence. It seems like a good reference manual for people who are already familiar with the fundamental concepts of commonly used probabilistic graphical models. However, it contains a lot|||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.Not only was the story interesting, engaging and relatable, it also teaches lessons.
