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Probabilistic Graphical Models: Principles and Techniques / Daphne Koller and Nir Friedman. p. cm. – (Adaptive computation and machine learning) Includes bibliographical references and index. ISBN 978-0-262-01319-2 (hardcover : alk. paper) 1. Graphical modeling (Statistics) 2. Bayesian statistical decision theory—Graphic methods. I. Koller ...
Probabilistic Graphical Models. Master a new way of reasoning and learning in complex domains. Taught in English. Some content may not be translated. Instructor: Daphne Koller. Enroll for Free. Starts May 4. Financial aid available. 25,504 already enrolled. About. Outcomes. Courses. Testimonials. Skills you'll gain. Inference. Bayesian Network.
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Jul 31, 2009 · Probabilistic Graphical Models. Principles and Techniques. by Daphne Koller and Nir Friedman. Hardcover. $135.00. Hardcover. ISBN: 9780262013192. Pub date: July 31, 2009. Publisher: The MIT Press. 1272 pp., 8 x 9 in, 399 b&w illus. MIT Press Bookstore Penguin Random House Amazon Barnes and Noble Bookshop.org Indiebound Indigo Books a Million. eBook
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Jul 31, 2009 · Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) [Koller, Daphne, Friedman, Nir] on Amazon.com. *FREE* shipping on qualifying offers.
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- Daphne Koller, Nir Friedman
- $91.22
- The MIT Press
- 1.1 Motivation
- 1.2 Structured Probabilistic Models
- 1.2.2 Representation, Inference, Learning
- 1.3.1 Overview of Chapters
- 1.3.2 Reader’s Guide
- 1.3.3 Connection to Other Disciplines
- 1.3.3.1 What Have We Gained?
declarative representation model Most tasks require a person or an automated system to reason: to take the available information and reach conclusions, both about what might be true in the world and about how to act. For example, a doctor needs to take information about a patient — his symptoms, test results, personal characteristics (gender, weigh...
This book describes a general-purpose framework for constructing and using probabilistic mod-els of complex systems. We begin by providing some intuition for the principles underlying this framework, and for the models it encompasses. This section requires some knowledge of basic concepts in probability theory; a reader unfamiliar with these concep...
inference The graphical language exploits structure that appears present in many distributions that we want to encode in practice: the property that variables tend to interact directly only with very few others. Distributions that exhibit this type of structure can generally be encoded naturally and compactly using a graphical model. This framework...
The framework of probabilistic graphical models is quite broad, and it encompasses both a variety of different types of models and a range of methods relating to them. This book describes several types of models. For each one, we describe the three fundamental cornerstones: representation, inference, and learning. We begin in part I, by describing ...
As we mentioned, the topics described in this book relate to multiple fields, and techniques from other disciplines — probability theory, computer science, information theory, optimization, statistics, and more — are used in various places throughout it. While it is impossible to present all of the relevant material within the scope of this book, w...
The ideas we describe in this book are connected to many fields. From probability theory, we inherit the basic concept of a probability distribution, as well as many of the operations we can use to manipulate it. From computer science, we exploit the key idea of using a graph as a data structure, as well as a variety of algorithms for manipulating ...
Although the framework we describe here shares common elements with a broad range of other topics, it has a coherent common core: the use of structure to allow a compact repre-sentation, effective reasoning, and feasible learning of general-purpose, factored, probabilistic models. These elements provide us with a general infrastructure for reasonin...
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.
Probabilistic Graphical Models. : Daphne Koller, Nir Friedman. MIT Press, Jul 31, 2009 - Computers - 1270 pages. A general framework for constructing and using probabilistic models of...
