Описание: This handbook brings together a comprehensive collection of mathematical material in one location. It also offers a variety of new results interpreted in a form that is particularly useful to engineers, scientists, and applied mathematicians.
Автор: Marcus Название: Markov Processes, Gaussian Processes, and Local Times ISBN: 1107403758 ISBN-13(EAN): 9781107403758 Издательство: Cambridge Academ Рейтинг: Цена: 12038.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Two foremost researchers present important advances in stochastic process theory by linking well-understood (Gaussian) and less well-understood (Markov) classes of processes. It builds to this material through `mini-courses` on the relevant ingredients, which assume only measure-theoretic probability. This original, readable 2006 book is for researchers and advanced graduate students.
Автор: Jondeau Eric Название: Financial Modeling Under Non-Gaussian Distributions ISBN: 1849965994 ISBN-13(EAN): 9781849965996 Издательство: Springer Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Practitioners and researchers who have handled financial market data know that asset returns do not behave according to the bell-shaped curve, associated with the Gaussian or normal distribution. Indeed, the use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. Consequently, non-Gaussian models and models based on processes with jumps, are gaining popularity among financial market practitioners.
Non-Gaussian distributions are the key theme of this book which addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. One of the main aims is to bridge the gap between the theoretical developments and the practical implementations of what many users and researchers perceive as "sophisticated" models or black boxes. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series, such as exchange and interest rates.
The authors have taken care to make the material accessible to anyone with a basic knowledge of statistics, calculus and probability, while at the same time preserving the mathematical rigor and complexity of the original models.
This book will be an essential reference for practitioners in the finance industry, especially those responsible for managing portfolios and monitoring financial risk, but it will also be useful for mathematicians who want to know more about how their mathematical tools are applied in finance, and as a text for advanced courses in empirical finance; financial econometrics and financial derivatives.
Автор: Bovier Название: Gaussian Processes on Trees ISBN: 1107160499 ISBN-13(EAN): 9781107160491 Издательство: Cambridge Academ Рейтинг: Цена: 9346.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Branching Brownian motion is a key model at the crossroads of value statistics for Gaussian processes, statistical physics, and non-linear partial differential equations. This book gives a concise introduction for graduate students and researchers leading up to the most recent developments in this active area of research.
Автор: Mandjes, Michel Название: Large deviations for gaussian queues ISBN: 0470015233 ISBN-13(EAN): 9780470015230 Издательство: Wiley Рейтинг: Цена: 17891.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Demonstrates how the Gaussian traffic model arises naturally, and how the analysis of the corresponding queuing model can be performed. This text provides an introduction to Gaussian queues, and surveys research into the modelling of communications networks. It is useful for postgraduate students in applied probability, and operations research.
Описание: This proceedings volume contains eight selected papers thatwere presented in the International Symposium in Statistics (ISS) 2015 OnAdvances in Parametric and Semi-parametric Analysis of Multivariate, TimeSeries, Spatial-temporal, and Familial-longitudinal Data, held in St. John`s,Canada from July 6 to 8, 2015.
Автор: Rue Название: Gaussian Markov Random Fields ISBN: 1584884320 ISBN-13(EAN): 9781584884323 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Gaussian Markov Random Field (GMRF) models, most widely used in spatial statistics are presented in this, the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects.
Автор: Mandrekar Название: Stochastic Analysis For Gaussian Ra ISBN: 1498707815 ISBN-13(EAN): 9781498707817 Издательство: Taylor&Francis Рейтинг: Цена: 15312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic properties connecting probabilistic notions. In particular, it studies Gaussian random fields using reproducing kernel Hilbert spaces (RKHSs).
The book begins with preliminary results on covariance and associated RKHS before introducing the Gaussian process and Gaussian random fields. The authors use chaos expansion to define the Skorokhod integral, which generalizes the It integral. They show how the Skorokhod integral is a dual operator of Skorokhod differentiation and the divergence operator of Malliavin. The authors also present Gaussian processes indexed by real numbers and obtain a Kallianpur-Striebel Bayes' formula for the filtering problem. After discussing the problem of equivalence and singularity of Gaussian random fields (including a generalization of the Girsanov theorem), the book concludes with the Markov property of Gaussian random fields indexed by measures and generalized Gaussian random fields indexed by Schwartz space. The Markov property for generalized random fields is connected to the Markov process generated by a Dirichlet form.
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