Analytic and Probabilistic Approaches to Dynamics in Negative Curvature, Fran?oise Dal`Bo; Marc Peign?; Andrea Sambusetti
Автор: Koller Daphne, Friedman Nir Название: Probabilistic Graphical Models: Principles and Techniques ISBN: 0262013193 ISBN-13(EAN): 9780262013192 Издательство: MIT Press Рейтинг: Цена: 21161.00 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: 12012.00 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: Pardoux Название: Probabilistic Models of Population Evolution ISBN: 3319303260 ISBN-13(EAN): 9783319303260 Издательство: Springer Рейтинг: Цена: 4611.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Описание: Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure.
The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: * Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students.
* Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.
Автор: Aravinda Название: Geometry, Topology, and Dynamics in Negative Curvature ISBN: 110752900X ISBN-13(EAN): 9781107529007 Издательство: Cambridge Academ Рейтинг: Цена: 11405.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Negative curvature arises in several mathematical areas, including geometry, topology, dynamics, and number theory. This volume contains survey articles around this common theme, which should help mathematicians interested in transitioning between these areas, as well as graduate students entering this interdisciplinary subject.
Автор: Sambusetti Andrea Название: Analytic and Probabilistic Approaches to Dynamics in Negativ ISBN: 3319048066 ISBN-13(EAN): 9783319048062 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The work consists of two introductory courses, developing different points of view on the study of the asymptotic behaviour of the geodesic flow, namely: the probabilistic approach via martingales and mixing (by Stephane Le Borgne);
Автор: Owen Dearricott; Fernando Galaz-Garc?a; Lee Kennar Название: Geometry of Manifolds with Non-negative Sectional Curvature ISBN: 3319063723 ISBN-13(EAN): 9783319063720 Издательство: Springer Рейтинг: Цена: 4890.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Riemannian manifolds with positive sectional curvature.- An introduction to isometric group actions.- A note on maximal symmetry rank, quasipositive curvature and low dimensional manifolds.- Lectures on n-Sasakian manifolds.- On the Hopf conjecture with symmetry.- An Introduction to Exterior Differential Systems.
Автор: Narens Louis Название: Probabilistic Lattices: With Applications To Psychology ISBN: 9814630411 ISBN-13(EAN): 9789814630412 Издательство: World Scientific Publishing Рейтинг: Цена: 12514.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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