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...
Download| Filename | Hy0iWF_unUW.pdf |
| Filetype | |
| Filesize | 31.25 MB |
| ISBN | 0 / |
| Pages | 1280 pages |
Click here to read or download to the file directly.