Instructor’s Manual for Probabilistic Graphical Models: Principles and Techniques
Authors: Daphne Koller, Nir Friedman
First product is official Instructor’s Manual for Probabilistic Graphical Models by Koller and Friedman. We can name it a Solution Manual because it includes answers of problems too.
File Specification for Instructor’s Manual
Extension | |
Pages | 59 |
Size | 0.5MB |
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Payment for First Product (Instructor’s Manual for Probabilistic Graphical Models by Koller and Friedman)
Second product is textbook for “Probabilistic Graphical Models by Daphne Koller and Nir Friedman”. Its specifications and the cover is available in following.
About the textbook: A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. 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. Read more
File Specification for Textbook
Extension | |
Pages | 1280 |
Size | 8MB |
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