Probabilistic Graphical Models

Author: Daphne Koller
Edition: 1
Binding: Hardcover
ISBN: 0262013193

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. Download Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) from rapidshare, mediafire, 4shared. 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 Mo Search and find a lot of computer books in many category availabe for free download.

Probabilistic Graphical Models Free

Probabilistic Graphical Models computer books for free. 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 he 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 Mo

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