Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
โ Scribed by Daphne Koller, Nir Friedman
- Publisher
- The MIT Press
- Year
- 2009
- Tongue
- English
- Leaves
- 1268
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
<span>An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.</span><span><br><br>An advanced counterpart to </span><span>Probabilistic Machi
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
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.