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КНИЖНЫЙ МИР

Probabilistic Graphical Models: Principles and Techniques   Daphne Koller, Nir Friedman

Probabilistic Graphical Models: Principles and Techniques

Adaptive Computation and Machine Learning
205x230 1280 страниц. 2009 год.
The MIT Press
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 constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of...
 
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