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 cons
Probabilistic Graphical Models: Principles and Techniques
โ Scribed by Daphne Koller, Nir Friedman
- Publisher
- The MIT Press
- Year
- 2009
- Tongue
- English
- Leaves
- 1280
- Series
- Adaptive Computation and Machine Learning series
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
1. Introduction -- 2. Foundations -- I. Representation -- 3. Bayesian Network Representation -- 4. Undirected Graphical Models -- 5. Local Probabilistic Models -- 6. Template-Based Representations -- 7. Gaussian Network Models -- 8. Exponential Family -- II. Inference -- 9. Exact Inference: Variabl
<p><b>A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.</b></p><p>Most tasks require a person or an automated system to reason -- to reach conclusions based on available information.
This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, graphical models, and deep learning, as wel
This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applicat