Introduction to Probability Theory and Stochastic Processes, Chiasson John
Автор: Kuo Название: Introduction to Stochastic Integration ISBN: 0387287205 ISBN-13(EAN): 9780387287201 Издательство: Springer Рейтинг: Цена: 5224 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The theory of stochastic integration, also called the Ito calculus, has a large spectrum of applications in virtually every scientific area involving random functions, but it can be a very difficult subject for people without much mathematical background. The Ito calculus was originally motivated by the construction of Markov diffusion processes from infinitesimal generators. Previously, the construction of such processes required several steps, whereas Ito constructed these diffusion processes directly in a single step as the solutions of stochastic integral equations associated with the infinitesimal generators. Moreover, the properties of these diffusion processes can be derived from the stochastic integral equations and the Ito formula. This introductory textbook on stochastic integration provides a concise introduction to the Ito calculus, and covers the following topics:* Constructions of Brownian motion;* Stochastic integrals for Brownian motion and martingales;* The Ito formula;* Multiple Wiener-Ito integrals;* Stochastic differential equations;* Applications to finance, filtering theory, and electric circuits.The reader should have a background in advanced calculus and elementary probability theory, as well as a basic knowledge of measure theory and Hilbert spaces. Each chapter ends with a variety of exercises designed to help the reader further understand the material.Hui-Hsiung Kuo is the Nicholson Professor of Mathematics at Louisiana State University. He has delivered lectures on stochastic integration at Louisiana State University, Cheng Kung University, Meijo University, and University of Rome "Tor Vergata," among others. He is also the author of Gaussian Measures in Banach Spaces (Springer 1975), and White Noise Distribution Theory (CRC Press 1996), and a memoir of his childhood growing up in Taiwan, An Arrow Shot into the Sun (Abridge Books 2004).
Описание: Provides an introduction to probability theory and its applications.
Автор: Gallager Название: Stochastic Processes ISBN: 1107039754 ISBN-13(EAN): 9781107039759 Издательство: Cambridge Academ Рейтинг: Цена: 4831 р. 6901.00-30% Наличие на складе: Есть (1 шт.) Описание: This definitive textbook provides a solid introduction to discrete and continuous stochastic processes, tackling a complex field in a way that instils a deep understanding of the relevant mathematical principles, and develops an intuitive grasp of the way these principles can be applied to modelling real-world systems. It includes a careful review of elementary probability and detailed coverage of Poisson, Gaussian and Markov processes with richly varied queuing applications. The theory and applications of inference, hypothesis testing, estimation, random walks, large deviations, martingales and investments are developed. Written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching and enriched by over 300 exercises, this is an exceptional resource for anyone looking to develop their understanding of stochastic processes.
Автор: Parzen, Emanuel Название: Stochastic processes ISBN: 0898714419 ISBN-13(EAN): 9780898714418 Издательство: Eurospan Рейтинг: Цена: 7874 р. Наличие на складе: Нет в наличии.
Описание: This introductory textbook explains how and why probability models are applied to scientific fields such as medicine, biology, physics, oceanography, economics, and psychology to solve problems about stochastic processes. It does not just show how a problem is solved but explains why by formulating questions and first steps in the solutions.
Описание: Discusses the theory of stochastic processes. This book presents basics of discrete time martingales. It includes such topics as Wiener process, stationary processes, infinitely divisible processes, and Ito stochastic equations.
Автор: Mark Pinsky Название: An Introduction to Stochastic Modeling, ISBN: 0123814162 ISBN-13(EAN): 9780123814166 Издательство: Elsevier Science Рейтинг: Цена: 8250 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Fourth Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of simple stochastic analysis to realistic problems. New to this edition: . . Realistic applications from a variety of disciplines integrated throughout the text, including more biological applications. Plentiful, completely updated problems. Completely updated and reorganized end-of-chapter exercise sets, 250 exercises with answers. New chapters of stochastic differential equations and Brownian motion and related processes. Additional sections on Martingale and Poisson process
Описание: Updated to conform to Mathematica® 7.0, this second edition shows how to easily create simulations from templates and solve problems using Mathematica. Along with new sections on order statistics, transformations of multivariate normal random variables, and Brownian motion, this edition offers an expanded section on Markov chains, more example data of the normal distribution, and more attention on conditional expectation. It also includes additional problems from Actuarial Exam P as well as new examples, exercises, and data sets. The accompanying CD-ROM contains updated Mathematica notebooks and a revised solutions manual is available for qualifying instructors.
Описание: This detailed introduction to probability and stochastic processes shows how these subjects may be applied to computer performance modelling. Readers are assumed to be familiar with elementary linear algebra and calculus, including the concept of limit.
Описание: Delineates stochastic processes, emphasizing applications in biology. This book is organized according to the three types of stochastic processes: discrete time Markov chains, continuous time Markov chains and continuous time and state Markov processes. It contains a chapter on the biological applications of stochastic differential equations.
Описание: Expanding on the first edition of An Introduction to Continuous-Time Stochastic Processes, this concisely written book is a rigorous and self-contained introduction to the theory of continuous-time stochastic processes. A balance of theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required.
Stochastic processes are indispensable tools for development and research in signal and image processing, automatic control, oceanography, structural reliability, environmetrics, climatology, econometrics, and many other areas of science and engineering. Suitable for a one-semester course, Stationary Stochastic Processes for Scientists and Engineers teaches students how to use these processes efficiently. Carefully balancing mathematical rigor and ease of exposition, the book provides students with a sufficient understanding of the theory and a practical appreciation of how it is used in real-life situations. Special emphasis is on the interpretation of various statistical models and concepts as well as the types of questions statistical analysis can answer.
The text first introduces numerous examples from signal processing, economics, and general natural sciences and technology. It then covers the estimation of mean value and covariance functions, properties of stationary Poisson processes, Fourier analysis of the covariance function (spectral analysis), and the Gaussian distribution. The book also focuses on input-output relations in linear filters, describes discrete-time auto-regressive and moving average processes, and explains how to solve linear stochastic differential equations. It concludes with frequency analysis and estimation of spectral densities.
With a focus on model building and interpreting the statistical concepts, this classroom-tested book conveys a broad understanding of the mechanisms that generate stationary stochastic processes. By combining theory and applications, the text gives students a well-rounded introduction to these processes. To enable hands-on practice, MATLAB(R) code is available online.
Описание: Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis. Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour - Gaussian and sparse - and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis. The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics. The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction. This is an ideal reference for graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistics.
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