Accelerated Life Models, Bagdonavicius, Vilijandas
Автор: Alfio Quarteroni Название: Numerical Models for Differential Problems ISBN: 3319493159 ISBN-13(EAN): 9783319493152 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces the basic concepts for the numerical modelling of partial differential equations. It details algorithmic and computer implementation aspects and provides a number of easy-to-use programs.
Автор: Guo, Xin , Lai, Tze Leung , Shek, Howard , Wong Название: Quantitative Trading ISBN: 0367871815 ISBN-13(EAN): 9780367871819 Издательство: Taylor&Francis Рейтинг: Цена: 63280.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part cove
Автор: Gulisashvili Название: Analytically Tractable Stochastic Stock Price Models ISBN: 3642312136 ISBN-13(EAN): 9783642312137 Издательство: Springer Цена: 37170.00 T Наличие на складе: Есть Описание: For instance, in the Hull-White model the volatility process is a geometric Brownian motion, the Stein-Stein model uses an Ornstein-Uhlenbeck process as the stochastic volatility, and in the Heston model a Cox-Ingersoll-Ross process governs the behavior of the volatility.
Автор: Gomes, Maria Isabel, Название: Mathematical models for decision making with multiple perspectives : ISBN: 0367440741 ISBN-13(EAN): 9780367440749 Издательство: Taylor&Francis Рейтинг: Цена: 153120.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book brings together, in a single volume, the fields of multicriteria decision making and multiobjective optimization that are traditionally covered by different books. It is written in a didactic form using examples to help understanding of the proposed methodologies better.
Автор: Erik W. Grafarend , Silvelyn Zwanzig , Joseph L. Awange Название: Applications of Linear and Nonlinear Models ISBN: 3030945979 ISBN-13(EAN): 9783030945978 Издательство: Springer Рейтинг: Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides numerous examples of linear and nonlinear model applications. Here, we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view and a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss–Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters, we concentrate on underdetermined and overdetermined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, BLUUE, BIQUE, BLE, BIQUE, and total least squares. The highlight is the simultaneous determination of the first moment and the second central moment of a probability distribution in an inhomogeneous multilinear estimation by the so-called E-D correspondence as well as its Bayes design. In addition, we discuss continuous networks versus discrete networks, use of Grassmann–Plucker coordinates, criterion matrices of type Taylor–Karman as well as FUZZY sets. Chapter seven is a speciality in the treatment of an overjet. This second edition adds three new chapters: (1) Chapter on integer least squares that covers (i) model for positioning as a mixed integer linear model which includes integer parameters. (ii) The general integer least squares problem is formulated, and the optimality of the least squares solution is shown. (iii) The relation to the closest vector problem is considered, and the notion of reduced lattice basis is introduced. (iv) The famous LLL algorithm for generating a Lovasz reduced basis is explained. (2) Bayes methods that covers (i) general principle of Bayesian modeling. Explain the notion of prior distribution and posterior distribution. Choose the pragmatic approach for exploring the advantages of iterative Bayesian calculations and hierarchical modeling. (ii) Present the Bayes methods for linear models with normal distributed errors, including noninformative priors, conjugate priors, normal gamma distributions and (iii) short outview to modern application of Bayesian modeling. Useful in case of nonlinear models or linear models with no normal distribution: Monte Carlo (MC), Markov chain Monte Carlo (MCMC), approximative Bayesian computation (ABC) methods. (3) Error-in-variables models, which cover: (i) Introduce the error-in-variables (EIV) model, discuss the difference to least squares estimators (LSE), (ii) calculate the total least squares (TLS) estimator. Summarize the properties of TLS, (iii) explain the idea of simulation extrapolation (SIMEX) estimators, (iv) introduce the symmetrized SIMEX (SYMEX) estimator and its relation to TLS, and (v) short outview to nonlinear EIV models. The chapter on algebraic solution of nonlinear system of equations has also been updated in line with the new emerging field of hybrid numeric-symbolic solutions to systems of nonlinear equations, ermined system of nonlinear equations on curved manifolds. The von Mises–Fisher distribution is characteristic for circular or (hyper) spherical data. Our last chapter is devoted to probabilistic regression, the special Gauss–Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation. A great part of the work is presented in four appendices. Appendix A is a treatment, of tensor algebra, namely linear algebra, matrix algebra, and multilinear algebra. Appendix B is devoted to sampling distributions and their use in terms of confidence intervals and confidence regions. Appendix C reviews the elementary notions of statistics, namely random events and stochastic processes. Appendix D introduces the basics of Groebner basis algebra, its careful definition, the Buchberger algorithm, especially the C. F. Gauss combinatorial algorithm.
Автор: Haibin Xie, Kuikui Fan, Shouyang Wang Название: Candlestick forecasting for investments ISBN: 0367703378 ISBN-13(EAN): 9780367703370 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Candlestick charts are often used in speculative markets to describe and forecast asset price movements. This book is the first of its kind to investigate candlestick charts and their statistical properties.
Автор: Zimmerman, Dale L. Название: Antedependence Models for Longitudinal Data ISBN: 1420064266 ISBN-13(EAN): 9781420064261 Издательство: Taylor&Francis Рейтинг: Цена: 224570.00 T Наличие на складе: Нет в наличии.
Автор: Mahmoud, Hosam Название: Polya Urn Models ISBN: 1420059831 ISBN-13(EAN): 9781420059830 Издательство: Taylor&Francis Рейтинг: Цена: 102080.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Chen Название: Stochastic Methods for Modeling and Predicting Complex Dynamical Systems ISBN: 3031222482 ISBN-13(EAN): 9783031222481 Издательство: Springer Рейтинг: Цена: 37260.00 T Наличие на складе: Поставка под заказ. Описание: This book enables readers to understand, model, and predict complex dynamical systems using new methods with stochastic tools. The author presents a unique combination of qualitative and quantitative modeling skills, novel efficient computational methods, rigorous mathematical theory, as well as physical intuitions and thinking. An emphasis is placed on the balance between computational efficiency and modeling accuracy, providing readers with ideas to build useful models in practice. Successful modeling of complex systems requires a comprehensive use of qualitative and quantitative modeling approaches, novel efficient computational methods, physical intuitions and thinking, as well as rigorous mathematical theories. As such, mathematical tools for understanding, modeling, and predicting complex dynamical systems using various suitable stochastic tools are presented. Both theoretical and numerical approaches are included, allowing readers to choose suitable methods in different practical situations. The author provides practical examples and motivations when introducing various mathematical and stochastic tools and merges mathematics, statistics, information theory, computational science, and data science. In addition, the author discusses how to choose and apply suitable mathematical tools to several disciplines including pure and applied mathematics, physics, engineering, neural science, material science, climate and atmosphere, ocean science, and many others. Readers will not only learn detailed techniques for stochastic modeling and prediction, but will develop their intuition as well. Important topics in modeling and prediction including extreme events, high-dimensional systems, and multiscale features are discussed.
Автор: Monahan, John F. Название: A Primer on Linear Models ISBN: 1420062018 ISBN-13(EAN): 9781420062014 Издательство: Taylor&Francis Рейтинг: Цена: 63280.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Chakravarthy, S. Название: Matrix-Analytic Methods in Stochastic Models ISBN: 0824797663 ISBN-13(EAN): 9780824797669 Издательство: Taylor&Francis Рейтинг: Цена: 275610.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
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