Автор: Koller Daphne, Friedman Nir Название: Probabilistic Graphical Models: Principles and Techniques ISBN: 0262013193 ISBN-13(EAN): 9780262013192 Издательство: MIT Press Рейтинг: Цена: 21161 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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.
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 models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Автор: Bass Название: Probabilistic Techniques in Analysis ISBN: 0387943870 ISBN-13(EAN): 9780387943879 Издательство: Springer Рейтинг: Цена: 12012 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Exploring the use of techniques drawn from probability research to tackle problems in mathematical analysis, this study includes discussion of the construction of the Martin boundary, Dahlberg`s Theorem, probabilistic proofs of the boundary Harnack principle, and much more.
Автор: Kelly Название: Bayesian Inference for Probabilistic Risk Assessment ISBN: 1849961867 ISBN-13(EAN): 9781849961868 Издательство: Springer Рейтинг: Цена: 23757 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems.
Описание: This unique book proposes a uniform logic and probabilistic (LP) approach to risk estimation and analysis in engineering and economics. It includes clear definitions and notations, revised chapters, an extended list of references, and a new subject index.
Автор: Cooper, George R.; McGillem, Clare D. Название: Probabilistic Methods of Signal and System Analysis ISBN: 0195123549 ISBN-13(EAN): 9780195123548 Издательство: Oxford Academ Рейтинг: Цена: 36430 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Originally published in 1971, this text is intended for signals and systems courses which emphasize probability. It provides an introduction to probability theory, statistics, random processes and the analysis of systems with random inputs. This edition has been updated and uses Matlab.
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