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Statistical Inference in Stochastic Processes, Prabhu, N.U.


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Цена: 16843.00р.
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Автор: Prabhu, N.U.
Название:  Statistical Inference in Stochastic Processes
ISBN: 9780824784171
Издательство: Taylor&Francis
Классификация:
ISBN-10: 0824784170
Обложка/Формат: Hardback
Страницы: 288
Вес: 0.50 кг.
Дата издания: 18.12.1990
Серия: Probability: pure and applied
Язык: English
Размер: 229 x 159 x 19
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Поставляется из: Европейский союз


The Elements of Statistical Learning

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 10480.00 р.
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Computer Age Statistical Inference, Student Edition

Автор: Bradley Efron , Trevor Hastie
Название: Computer Age Statistical Inference, Student Edition
ISBN: 1108823416 ISBN-13(EAN): 9781108823418
Издательство: Cambridge Academ
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Цена: 5069.00 р.
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Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Weak Convergence of Stochastic Processes: With Applications to Statistical Limit Theorems

Автор: Vidyadhar S. Mandrekar
Название: Weak Convergence of Stochastic Processes: With Applications to Statistical Limit Theorems
ISBN: 3110475421 ISBN-13(EAN): 9783110475425
Издательство: Walter de Gruyter
Цена: 11148.00 р.
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Описание: The purpose of this book is to present results on the subject of weak convergence in function spaces to study invariance principles in statistical applications to dependent random variables, U-statistics, censor data analysis. Different techniques, formerly available only in a broad range of literature, are for the first time presented here in a self-contained fashion. Contents:Weak convergence of stochastic processesWeak convergence in metric spacesWeak convergence on C[0, 1] and D[0,?)Central limit theorem for semi-martingales and applicationsCentral limit theorems for dependent random variablesEmpirical processBibliography

Perspectives In Mathematical Science I: Probability And Statistics

Автор: Sastry N S Narasimha Et Al
Название: Perspectives In Mathematical Science I: Probability And Statistics
ISBN: 9814273627 ISBN-13(EAN): 9789814273626
Издательство: World Scientific Publishing
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Цена: 13464.00 р.
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Описание: A collection of invited articles by distinguished probabilists and statisticians on the occasion of the Platinum Jubilee Celebrations of the Indian Statistical Institute. It covers topics in probability and statistics, and offers a perspective of different areas of research, emphasizing the major challenging issues.

Stochastic processes

Автор: Jones, Peter Watts Smith, Peter
Название: Stochastic processes
ISBN: 0367657600 ISBN-13(EAN): 9780367657604
Издательство: Taylor&Francis
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Цена: 7654.00 р.
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Описание: This is the third edition of a popular UK textbook on stochastic processes. It has been updated with a new chapter on diffusion processes and Brownian motion, has extended material on birth and death processes and epidemics, plus new references throughout. The examples and exercises have all been expanded and improved.

Stochastic Processes with R: An Introduction

Автор: Korosteleva Olga
Название: Stochastic Processes with R: An Introduction
ISBN: 1032153733 ISBN-13(EAN): 9781032153735
Издательство: Taylor&Francis
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Цена: 13779.00 р.
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Описание: Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes.

Stochastic Processes, Statistical Methods, and Engineering Mathematics

Автор: Malyarenko
Название: Stochastic Processes, Statistical Methods, and Engineering Mathematics
ISBN: 303117819X ISBN-13(EAN): 9783031178191
Издательство: Springer
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Цена: 39130.00 р.
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Описание: The goal of the 2019 conference on Stochastic Processes and Algebraic Structures held in SPAS2019, V?ster?s, Sweden, from September 30th to October 2nd 2019, was to showcase the frontiers of research in several important areas of mathematics, mathematical statistics, and its applications. The conference was organized around the following topics 1. Stochastic processes and modern statistical methods, 2. Engineering mathematics, 3. Algebraic structures and their applications. The conference brought together a select group of scientists, researchers, and practitioners from the industry who are actively contributing to the theory and applications of stochastic, and algebraic structures, methods, and models. The conference provided early stage researchers with the opportunity to learn from leaders in the field, to present their research, as well as to establish valuable research contacts in order to initiate collaborations in Sweden and abroad. New methods for pricing sophisticated financial derivatives, limit theorems for stochastic processes, advanced methods for statistical analysis of financial data, and modern computational methods in various areas of applied science can be found in this book. The principal reason for the growing interest in these questions comes from the fact that we are living in an extremely rapidly changing and challenging environment. This requires the quick introduction of new methods, coming from different areas of applied science. Advanced concepts in the book are illustrated in simple form with the help of tables and figures. Most of the papers are self-contained, and thus ideally suitable for self-study. Solutions to sophisticated problems located at the intersection of various theoretical and applied areas of the natural sciences are presented in these proceedings.

Stationary Stochastic Processes

Автор: Lindgren, Georg
Название: Stationary Stochastic Processes
ISBN: 1466557796 ISBN-13(EAN): 9781466557796
Издательство: Taylor&Francis
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Цена: 15312.00 р.
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Markov Chain Monte Carlo

Автор: Gamerman, Dani.
Название: Markov Chain Monte Carlo
ISBN: 1584885874 ISBN-13(EAN): 9781584885870
Издательство: Taylor&Francis
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Цена: 15312.00 р.
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Описание: Incorporating changes in theory and highlighting various applications, this book presents a comprehensive introduction to the methods of Markov Chain Monte Carlo (MCMC) simulation technique. It incorporates the developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection.

Statistical Inference for Ergodic Diffusion Processes

Автор: Yury A. Kutoyants
Название: Statistical Inference for Ergodic Diffusion Processes
ISBN: 184996906X ISBN-13(EAN): 9781849969062
Издательство: Springer
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Цена: 21661.00 р.
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Описание: The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.

Point Processes and Their Statistical Inference

Автор: Karr, Alan
Название: Point Processes and Their Statistical Inference
ISBN: 0367580039 ISBN-13(EAN): 9780367580032
Издательство: Taylor&Francis
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Цена: 7501.00 р.
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Описание: Maintaining the excellent features that made the first edition so popular, this outstanding reference/text presents the only comprehensive treatment of the theory of point processes and statistical inference for point processes.

Ruin Probabilities (2Nd Edition)

Автор: Asmussen Soren Et Al
Название: Ruin Probabilities (2Nd Edition)
ISBN: 9813203617 ISBN-13(EAN): 9789813203617
Издательство: World Scientific Publishing
Цена: 12989.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание:

The book gives a comprehensive treatment of the classical and modern ruin probability theory. Some of the topics are Lundberg's inequality, the CramEr-Lundberg approximation, exact solutions, other approximations (e.g., for heavy-tailed claim size distributions), finite horizon ruin probabilities, extensions of the classical compound Poisson model to allow for reserve-dependent premiums, Markov-modulation, periodicity, change of measure techniques, phase-type distributions as a computational vehicle and the connection to other applied probability areas, like queueing theory. In this substantially updated and extended second version, new topics include stochastic control, fluctuation theory for Levy processes, Gerber-Shiu functions and dependence.


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