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Probabilistic Logics and Probabilistic Networks, Haenni



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Автор: Haenni
Название:  Probabilistic Logics and Probabilistic Networks
ISBN: 9789400700079
Издательство: Springer
Классификация:
ISBN-10: 9400700075
Обложка/Формат: Hardback
Страницы: 172
Вес: 0.496 кг.
Дата издания: 02.12.2010
Серия: Synthese Library Vol. 350
Язык: English
Издание: 1st Ed.
Иллюстрации: Black & white illustrations
Размер: 243 x 166 x 14
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии



Probabilistic Graphical Models: Principles and Techniques

Автор: Koller Daphne, Friedman Nir
Название: Probabilistic Graphical Models: Principles and Techniques
ISBN: 0262013193 ISBN-13(EAN): 9780262013192
Издательство: MIT Press
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Цена: 13695 р.
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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.

Probabilistic Logics and Probabilistic Networks

Автор: Rolf Haenni; Jan-Willem Romeijn; Gregory Wheeler;
Название: Probabilistic Logics and Probabilistic Networks
ISBN: 9400734433 ISBN-13(EAN): 9789400734432
Издательство: Springer
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Цена: 12704 р.
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Описание: Probabilistic Logic and Probabilistic Networks presents a groundbreaking framework within which various approaches to probabilistic logic naturally fit. Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.

Probabilistic Techniques in Analysis

Автор: Bass
Название: Probabilistic Techniques in Analysis
ISBN: 0387943870 ISBN-13(EAN): 9780387943879
Издательство: Springer
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Цена: 9927 р.
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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.

Probabilistic networks and expert systems

Автор: Cowell, R.g. Dawid, A. Philip Lauritzen, S.l. Spie
Название: Probabilistic networks and expert systems
ISBN: 0387718230 ISBN-13(EAN): 9780387718231
Издательство: Springer
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Цена: 12126 р.
Наличие на складе: Поставка под заказ.

Описание: Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on research by the authors over a number of years, this book gives a mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases.

Probabilistic Methods in Geotechnical Engineering

Автор: Li
Название: Probabilistic Methods in Geotechnical Engineering
ISBN: 9054103035 ISBN-13(EAN): 9789054103035
Издательство: Taylor&Francis
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Цена: 35750 р.
Наличие на складе: Невозможна поставка.

Описание: The proceedings of this conference contain keynote addresses on recent developments in geotechnical reliability and limit state design in geotechnics.

Probabilistic Lattices: With Applications To Psychology

Автор: Narens Louis
Название: Probabilistic Lattices: With Applications To Psychology
ISBN: 9814630411 ISBN-13(EAN): 9789814630412
Издательство: World Scientific Publishing
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Цена: 10819 р.
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Описание: There Are Many Books On Lattice Theory In The Field, But None Interfaces With The Foundations Of Probability. This Book Does. It Also Develops New Probability Theories With Rigorous Foundations For Decision Theory And Applies Them To Specific Well-Known Problematic Examples. There Is Only One Other Book That Attempts This. It Uses Quantum Probability Theory From Physics. The New Probability Theories Developed In This Book Are Different; They Are Not Borrowed From Physics But Are Explicitly Designed For Decision Theory.

Probabilistic Forecasting and Bayesian Data Assimilation

Автор: Reich
Название: Probabilistic Forecasting and Bayesian Data Assimilation
ISBN: 1107663911 ISBN-13(EAN): 9781107663916
Издательство: Cambridge Academ
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Цена: 5614 р.
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Описание: In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.

Probabilistic Forecasting and Bayesian Data Assimilation

Автор: Reich
Название: Probabilistic Forecasting and Bayesian Data Assimilation
ISBN: 1107069394 ISBN-13(EAN): 9781107069398
Издательство: Cambridge Academ
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Цена: 14380 р.
Наличие на складе: Поставка под заказ.

Описание: In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.

Probabilistic Design for Optimization and Robustness for Eng

Автор: Dodson Bryan
Название: Probabilistic Design for Optimization and Robustness for Eng
ISBN: 1118796195 ISBN-13(EAN): 9781118796191
Издательство: Wiley
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Цена: 9893 р.
Наличие на складе: Поставка под заказ.

Описание: How to apply robust design to engineering design problems Unlike the Taguchi approach to robustness, which requires experimentation, the approach described in this book takes advantage of engineering knowledge to create models for system variation. Pro

The Probabilistic Method, 2nd Edition

Автор: Noga Alon
Название: The Probabilistic Method, 2nd Edition
ISBN: 0471370460 ISBN-13(EAN): 9780471370468
Издательство: Wiley
Цена: 9343 р.
Наличие на складе: Поставка под заказ.

Описание: Initiated by Paul Erdos, the probabilistic method allows us to prove the existence of a combinatorial structure with certain properties by constructing an appropriate probability space and showing that a randomlhosen element in this space has the desired properties with positive probability. The volume is a revision of the leading reference on probabilistic methods in combinatorics.

Автор: Baldi
Название: Modeling the Internet and the Web - Probabilistic Methods and Algorithms 2e
ISBN: 0470663774 ISBN-13(EAN): 9780470663776
Издательство: Wiley
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Цена: 6875 р.
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Описание: This book covers the most important aspects of the web using modern mathematical and probabilistic treatment.

Probabilistic Models of Population Evolution

Автор: Pardoux
Название: Probabilistic Models of Population Evolution
ISBN: 3319303260 ISBN-13(EAN): 9783319303260
Издательство: Springer
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Цена: 3810 р.
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Описание: This expository book presents the mathematical description of evolutionary models of populations subject to interactions (e.g. competition) within the population. The author includes both models of finite populations, and limiting models as the size of the population tends to infinity. The size of the population is described as a random function of time and of the initial population (the ancestors at time 0). The genealogical tree of such a population is given. Most models imply that the population is bound to go extinct in finite time. It is explained when the interaction is strong enough so that the extinction time remains finite, when the ancestral population at time 0 goes to infinity. The material could be used for teaching stochastic processes, together with their applications.

?tienne Pardoux is Professor at Aix-Marseille University, working in the field of Stochastic Analysis, stochastic partial differential equations, and probabilistic models in evolutionary biology and population genetics. He obtained his PhD in 1975 at University of Paris-Sud.

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