Stochastic Models In The Life Sciences And Their Methods Of Analysis, Wan Frederic Y M

Автор: Rahimi Tabar M. Reza
Название: Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems: Using the Methods of Stochastic Processes
ISBN: 3030184749 ISBN-13(EAN): 9783030184742
Издательство: Springer
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Цена: 107130.00 T
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Описание:
​1 Introduction.- 2 Introduction to Stochastic Processes.- 3 Kramers-Moyal Expansion and Fokker-Planck Equation.- 4 Continuous Stochastic Process.- 5 The Langevin Equation and Wiener Process.- 6 Stochastic Integration, It o and Stratonovich Calculi.- 7 Equivalence of Langevin and Fokker-Planck Equations.- 8 Examples of Stochastic Calculus.-9 Langevin Dynamics in Higher Dimensions.- 10 Levy Noise Driven Langevin Equation and its Time Series-Based Reconstruction.- 11 Stochastic Processes with Jumps and Non-Vanishing Higher-Order Kramers-Moyal Coefficients.- 12 Jump-Diffusion Processes.- 13 Two-Dimensional (Bivariate) Jump-Diffusion Processes.- 14 Numerical Solution of Stochastic Differential Equations: Diffusion and Jump-Diffusion Processes.- 15 The Friedrich-Peinke Approach to Reconstruction of Dynamical Equation for Time Series: Complexity in View of Stochastic Processes.- 16 How To Set Up Stochastic Equations For Real-World Processes: Markov-Einstein Time Scale.- 17 Reconstruction of Stochastic Dynamical Equations: Exemplary Stationary Diffusion and Jump-Diffusion Processes.- 18 The Kramers-Moyal Coefficients of Non-Stationary Time series in The Presence of Microstructure (Measurement) Noise.- 19 Influence of Finite Time Step in Estimating of the Kramers-Moyal Coefficients.- 20 Distinguishing Diffusive and Jumpy Behaviors in Real-World Time Series.- 21 Reconstruction of Langevin and Jump-Diffusion Dynamics From Empirical Uni- and Bivariate Time Series.- 22 Applications and Outlook.- 23 Epileptic Brain Dynamics.

Автор: Ren? Carmona; M R Tehranchi
Название: Interest Rate Models: an Infinite Dimensional Stochastic Analysis Perspective
ISBN: 3642066003 ISBN-13(EAN): 9783642066009
Издательство: Springer
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Цена: 69870.00 T
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Описание: This book presents the mathematical issues that arise in modeling the interest rate term structure by casting the interest-rate models as stochastic evolution equations in infinite dimensions.

Автор: Box-Steffensmeier
Название: Time Series Analysis for the Social Sciences
ISBN: 0521691559 ISBN-13(EAN): 9780521691550
Издательство: Cambridge Academ
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Цена: 30610.00 T
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Описание: Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. The book covers ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting.

Автор: Bernido Christopher C & Carpio-Bernido M Victoria
Название: Methods And Applications Of White Noise Analysis In Interdisciplinary Sciences
ISBN: 9814569119 ISBN-13(EAN): 9789814569118
Издательство: World Scientific Publishing
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Цена: 83430.00 T
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Analysis, modeling, and simulation for better understanding of diverse complex natural and social phenomena often require powerful tools and analytical methods. Tractable approaches, however, can be developed with mathematics beyond the common toolbox. This book presents the white noise stochastic calculus, originated by T Hida, as a novel and powerful tool in investigating physical and social systems. The calculus, when combined with Feynman's summation-over-all-histories, has opened new avenues for resolving cross-disciplinary problems. Applications to real-world complex phenomena are further enhanced by parametrizing non-Markovian evolution of a system with various types of memory functions. This book presents general methods and applications to problems encountered in complex systems, scaling in industry, neuroscience, polymer physics, biophysics, time series analysis, relativistic and nonrelativistic quantum systems.

Автор: Gobet
Название: Monte-Carlo Methods & Stochastic Pr
ISBN: 1498746225 ISBN-13(EAN): 9781498746229
Издательство: Taylor&Francis
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Цена: 91860.00 T
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Developed from the author's course at the Ecole Polytechnique, Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear and nonlinear problems in biology, finance, geophysics, mechanics, chemistry, and other application areas. The text also thoroughly develops the problem of numerical integration and computation of expectation by the Monte-Carlo method.

The book begins with a history of Monte-Carlo methods and an overview of three typical Monte-Carlo problems: numerical integration and computation of expectation, simulation of complex distributions, and stochastic optimization. The remainder of the text is organized in three parts of progressive difficulty. The first part presents basic tools for stochastic simulation and analysis of algorithm convergence. The second part describes Monte-Carlo methods for the simulation of stochastic differential equations. The final part discusses the simulation of non-linear dynamics.

Автор: J.S. Byrnes; Kathryn A. Hargreaves; Karl Berry
Название: Probabilistic and Stochastic Methods in Analysis, with Applications
ISBN: 0792318048 ISBN-13(EAN): 9780792318040
Издательство: Springer
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Цена: 437900.00 T
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Описание: Contains three expositions on wavelets, frames and their applications. This book includes the relation between probability and partial differential equations, including probabilistic representations of solutions to elliptic and parabolic PDEs.

Автор: Yoon-Jae Whang
Название: Econometric Analysis of Stochastic Dominance: Concepts, Methods, Tools, and Applications
ISBN: 1108472796 ISBN-13(EAN): 9781108472791
Издательство: Cambridge Academ
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Цена: 61240.00 T
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Описание: Stochastic dominance is a fundamental concept used heavily in various fields of science such as economics, finance, insurance, medicine, and statistics. This book examines stochastic dominance in a unified framework, focusing on inferential methods and foundations. It will appeal to graduate students, academic researchers, and professionals.

Автор: Tan Wai-Yuan
Название: Stochastic Models With Applications To Genetics, Cancers, Aids And Other Biomedical Systems (Second Edition)
ISBN: 9814390941 ISBN-13(EAN): 9789814390941
Издательство: World Scientific Publishing
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Цена: 174240.00 T
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Описание: Presents a systematic treatment of Markov chains, diffusion processes and state space models, as well as alternative approaches to Markov chains through stochastic difference equations and stochastic differential equations. This title illustrates how these processes and approaches are applied to many problems in genetics, carcinogenesis, and more.

Автор: Box-Steffensmeier
Название: Time Series Analysis for the Social Sciences
ISBN: 0521871166 ISBN-13(EAN): 9780521871167
Издательство: Cambridge Academ
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Цена: 82370.00 T
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Описание: Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. The book covers ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting.

Автор: Muller
Название: Comparison Methods for Stochastic Models & Risks
ISBN: 0471494461 ISBN-13(EAN): 9780471494461
Издательство: Wiley
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Цена: 153070.00 T
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Описание: This work covers stochastic order relations, which provide insight into the behaviour of complex stochastic (random) systems and enables the user to collect comparative data. Application areas include queuing systems, actuarial and financial risk, decision making, and stochastic simulation.

Автор: Zhihua Zhang
Название: Environmental Data Analysis: Methods and Applications
ISBN: 3110430010 ISBN-13(EAN): 9783110430011
Издательство: Walter de Gruyter
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Цена: 111510.00 T
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Описание: Most environmental data involve a large degree of complexity and uncertainty. Environmental Data Analysis is created to provide modern quantitative tools and techniques designed specifically to meet the needs of environmental sciences and related fields. This book has an impressive coverage of the scope. Main techniques described in this book are models for linear and nonlinear environmental systems, statistical & numerical methods, data envelopment analysis, risk assessments and life cycle assessments. These state-of-the-art techniques have attracted significant attention over the past decades in environmental monitoring, modeling and decision making. Environmental Data Analysis explains carefully various data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that is accessible to researchers and advanced students in science and engineering. This is an excellent reference for scientists and engineers who wish to analyze, interpret and model data from various sources, and is also an ideal graduate-level textbook for courses in environmental sciences and related fields. Contents: PrefaceTime series analysisChaos and dynamical systemsApproximationInterpolationStatistical methodsNumerical methodsOptimizationData envelopment analysisRisk assessmentsLife cycle assessmentsIndex

Автор: Samuel N. Cohen; Istv?n Gy?ngy; Gon?alo dos Reis;
Название: Frontiers in Stochastic Analysis–BSDEs, SPDEs and their Applications
ISBN: 3030222845 ISBN-13(EAN): 9783030222840
Издательство: Springer
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Цена: 158380.00 T
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Описание: This collection of selected, revised and extended contributions resulted from a Workshop on BSDEs, SPDEs and their Applications that took place in Edinburgh, Scotland, July 2017 and included the 8th World Symposium on BSDEs. The volume addresses recent advances involving backward stochastic differential equations (BSDEs) and stochastic partial differential equations (SPDEs). These equations are of fundamental importance in modelling of biological, physical and economic systems, and underpin many problems in control of random systems, mathematical finance, stochastic filtering and data assimilation. The papers in this volume seek to understand these equations, and to use them to build our understanding in other areas of mathematics.This volume will be of interest to those working at the forefront of modern probability theory, both established researchers and graduate students.