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Modeling and Reasoning with Bayesian Networks, Darwiche


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Автор: Darwiche
Название:  Modeling and Reasoning with Bayesian Networks
Перевод названия: Аднан Дарвиче: Моделирование и обоснование Байесовских сетей
ISBN: 9781107678422
Издательство: Cambridge Academ
Классификация:



ISBN-10: 1107678420
Обложка/Формат: Paperback
Страницы: 562
Вес: 0.98 кг.
Дата издания: 07.08.2014
Серия: Mathematics
Язык: English
Иллюстрации: Worked examples or exercises; 64 tables, unspecified; 10 halftones, unspecified; 236 line drawings, unspecified
Размер: 254 x 180 x 29
Читательская аудитория: Professional and scholarly
Ключевые слова: Probability & statistics,Mathematical theory of computation, COMPUTERS / Natural Language Processing
Основная тема: Computer science
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Англии
Описание: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.

Analytical Modeling of Heterogeneous Cellular Networks

Автор: Mukherjee
Название: Analytical Modeling of Heterogeneous Cellular Networks
ISBN: 1107050944 ISBN-13(EAN): 9781107050945
Издательство: Cambridge Academ
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Цена: 58070.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive, self-contained introduction to the use of stochastic geometry techniques for studying the behaviour of heterogeneous cellular networks presents a range of analytic results and approaches, including the mathematical tools and techniques used to derive these results. A valuable reference for industry practitioners, graduate students and researchers.

Bayesian Reasoning and Machine Learning

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
Рейтинг:
Цена: 73920.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.

Bayesian Modeling Using WinBUGS

Автор: Ntzoufras, Ioannis
Название: Bayesian Modeling Using WinBUGS
ISBN: 047014114X ISBN-13(EAN): 9780470141144
Издательство: Wiley
Рейтинг:
Цена: 147790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Detailed examples will be provided ranging from the very basic to the more advanced; they will also reflect realistic data sets (available from the Internet). An underlying emphasis is given to Generalized Linear Models (GLMs) that are familiar to most readers and researchers.

Bayesian Analysis with Stata

Автор: Thompson John
Название: Bayesian Analysis with Stata
ISBN: 1597181412 ISBN-13(EAN): 9781597181419
Издательство: Taylor&Francis
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Цена: 57150.00 T
Наличие на складе: Невозможна поставка.
Описание:

Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata's data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.

The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.

The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.


Modeling and Optimization for Mobile Social Networks

Автор: Zhou Su; Qichao Xu; Kuan Zhang; Xuemin (Sherman) S
Название: Modeling and Optimization for Mobile Social Networks
ISBN: 3319479210 ISBN-13(EAN): 9783319479217
Издательство: Springer
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Цена: 74530.00 T
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Описание:

This book investigates the modeling and optimization issues in mobile social networks (MSNs). Firstly, the architecture and applications of MSNs are examined. The existing works on MSNs are reviewed by specifying the critical challenges and research issues. Then, with the introduction of MSN-based social graph and information dissemination mechanisms, the analytical model for epidemic information dissemination with opportunistic Links in MSNs is discussed. In addition, optimal resource allocation is studied based on a heterogeneous architecture, which provides mobile social services with high capacity and low latency. Finally, this book summarize some open problems and future research directions in MSNs.
Written for researchers and academics, this book is useful for anyone working on mobile networks, network architecture, or content delivery. It is also valuable for advanced-level students of computer science.

Bayesian Psychometric Modeling

Автор: Levy Roy, Mislevy Robert J.
Название: Bayesian Psychometric Modeling
ISBN: 1439884676 ISBN-13(EAN): 9781439884676
Издательство: Taylor&Francis
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Цена: 93910.00 T
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Описание:

A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment

Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics.

Adopting a Bayesian approach can aid in unifying seemingly disparate-and sometimes conflicting-ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking.

The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.



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