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The Resource Analysis and DataBased Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes, by M. Reza Rahimi Tabar, (electronic resource)
Analysis and DataBased Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes, by M. Reza Rahimi Tabar, (electronic resource)
Resource Information
The item Analysis and DataBased Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes, by M. Reza Rahimi Tabar, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Analysis and DataBased Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes, by M. Reza Rahimi Tabar, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 Summary
 This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni or multivariant sequentially measured set of experimental data (even noisy data), how should one analyse nonparametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation? Here, the term "nonparametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data. The book provides an overview of methods that have been developed for the analysis of fluctuating time series and of spatially disordered structures. Thanks to its feasibility and simplicity, it has been successfully applied to fluctuating time series and spatially disordered structures of complex systems studied in scientific fields such as physics, astrophysics, meteorology, earth science, engineering, finance, medicine and the neurosciences, and has led to a number of important results. The book also includes the numerical and analytical approaches to the analyses of complex time series that are most common in the physical and natural sciences. Further, it is selfcontained and readily accessible to students, scientists, and researchers who are familiar with traditional methods of mathematics, such as ordinary, and partial differential equations. The codes for analysing continuous time series are available in an R package developed by the research group Turbulence, Wind energy and Stochastic (TWiSt) at the Carl von Ossietzky University of Oldenburg under the supervision of Prof. Dr. Joachim Peinke. This package makes it possible to extract the (stochastic) evolution equation underlying a set of data or measurements
 Language
 eng
 Edition
 1st ed. 2019.
 Extent
 XVIII, 280 p. 41 illus., 22 illus. in color.
 Contents

 1 Introduction
 2 Introduction to Stochastic Processes
 3 KramersMoyal Expansion and FokkerPlanck 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 FokkerPlanck Equations
 8 Examples of Stochastic Calculus
 9 Langevin Dynamics in Higher Dimensions
 10 Levy Noise Driven Langevin Equation and its Time SeriesBased Reconstruction
 11 Stochastic Processes with Jumps and NonVanishing HigherOrder KramersMoyal Coefficients
 12 JumpDiffusion Processes
 13 TwoDimensional (Bivariate) JumpDiffusion Processes
 14 Numerical Solution of Stochastic Differential Equations: Diffusion and JumpDiffusion Processes
 15 The FriedrichPeinke Approach to Reconstruction of Dynamical Equation for Time Series: Complexity in View of Stochastic Processes
 16 How To Set Up Stochastic Equations For RealWorld Processes: MarkovEinstein Time Scale
 17 Reconstruction of Stochastic Dynamical Equations: Exemplary Stationary Diffusion and JumpDiffusion Processes
 18 The KramersMoyal Coefficients of NonStationary Time series in The Presence of Microstructure (Measurement) Noise
 19 Influence of Finite Time Step in Estimating of the KramersMoyal Coefficients
 20 Distinguishing Diffusive and Jumpy Behaviors in RealWorld Time Series
 21 Reconstruction of Langevin and JumpDiffusion Dynamics From Empirical Uni and Bivariate Time Series
 22 Applications and Outlook
 23 Epileptic Brain Dynamics
 Isbn
 9783030184728
 Label
 Analysis and DataBased Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes
 Title
 Analysis and DataBased Reconstruction of Complex Nonlinear Dynamical Systems
 Title remainder
 Using the Methods of Stochastic Processes
 Statement of responsibility
 by M. Reza Rahimi Tabar
 Language
 eng
 Summary
 This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni or multivariant sequentially measured set of experimental data (even noisy data), how should one analyse nonparametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation? Here, the term "nonparametrically" exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data. The book provides an overview of methods that have been developed for the analysis of fluctuating time series and of spatially disordered structures. Thanks to its feasibility and simplicity, it has been successfully applied to fluctuating time series and spatially disordered structures of complex systems studied in scientific fields such as physics, astrophysics, meteorology, earth science, engineering, finance, medicine and the neurosciences, and has led to a number of important results. The book also includes the numerical and analytical approaches to the analyses of complex time series that are most common in the physical and natural sciences. Further, it is selfcontained and readily accessible to students, scientists, and researchers who are familiar with traditional methods of mathematics, such as ordinary, and partial differential equations. The codes for analysing continuous time series are available in an R package developed by the research group Turbulence, Wind energy and Stochastic (TWiSt) at the Carl von Ossietzky University of Oldenburg under the supervision of Prof. Dr. Joachim Peinke. This package makes it possible to extract the (stochastic) evolution equation underlying a set of data or measurements
 http://library.link/vocab/creatorName
 Rahimi Tabar, M. Reza
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
 lqzdfMiq2hA
 Image bit depth
 0
 LC call number
 QC174.7175.36
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName
 SpringerLink
 Series statement
 Understanding Complex Systems,
 http://library.link/vocab/subjectName

 Distribution (Probability theory)
 Economics
 Engineering
 Neurosciences
 Complex Systems
 Complex Systems
 Probability Theory and Stochastic Processes
 Economic Theory/Quantitative Economics/Mathematical Methods
 Complexity
 Neurosciences
 Label
 Analysis and DataBased Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes, by M. Reza Rahimi Tabar, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 1 Introduction  2 Introduction to Stochastic Processes  3 KramersMoyal Expansion and FokkerPlanck 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 FokkerPlanck Equations  8 Examples of Stochastic Calculus  9 Langevin Dynamics in Higher Dimensions  10 Levy Noise Driven Langevin Equation and its Time SeriesBased Reconstruction  11 Stochastic Processes with Jumps and NonVanishing HigherOrder KramersMoyal Coefficients  12 JumpDiffusion Processes  13 TwoDimensional (Bivariate) JumpDiffusion Processes  14 Numerical Solution of Stochastic Differential Equations: Diffusion and JumpDiffusion Processes  15 The FriedrichPeinke Approach to Reconstruction of Dynamical Equation for Time Series: Complexity in View of Stochastic Processes  16 How To Set Up Stochastic Equations For RealWorld Processes: MarkovEinstein Time Scale  17 Reconstruction of Stochastic Dynamical Equations: Exemplary Stationary Diffusion and JumpDiffusion Processes  18 The KramersMoyal Coefficients of NonStationary Time series in The Presence of Microstructure (Measurement) Noise  19 Influence of Finite Time Step in Estimating of the KramersMoyal Coefficients  20 Distinguishing Diffusive and Jumpy Behaviors in RealWorld Time Series  21 Reconstruction of Langevin and JumpDiffusion Dynamics From Empirical Uni and Bivariate Time Series  22 Applications and Outlook  23 Epileptic Brain Dynamics
 Dimensions
 unknown
 Edition
 1st ed. 2019.
 Extent
 XVIII, 280 p. 41 illus., 22 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783030184728
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783030184728
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783030184728
 Label
 Analysis and DataBased Reconstruction of Complex Nonlinear Dynamical Systems : Using the Methods of Stochastic Processes, by M. Reza Rahimi Tabar, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 1 Introduction  2 Introduction to Stochastic Processes  3 KramersMoyal Expansion and FokkerPlanck 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 FokkerPlanck Equations  8 Examples of Stochastic Calculus  9 Langevin Dynamics in Higher Dimensions  10 Levy Noise Driven Langevin Equation and its Time SeriesBased Reconstruction  11 Stochastic Processes with Jumps and NonVanishing HigherOrder KramersMoyal Coefficients  12 JumpDiffusion Processes  13 TwoDimensional (Bivariate) JumpDiffusion Processes  14 Numerical Solution of Stochastic Differential Equations: Diffusion and JumpDiffusion Processes  15 The FriedrichPeinke Approach to Reconstruction of Dynamical Equation for Time Series: Complexity in View of Stochastic Processes  16 How To Set Up Stochastic Equations For RealWorld Processes: MarkovEinstein Time Scale  17 Reconstruction of Stochastic Dynamical Equations: Exemplary Stationary Diffusion and JumpDiffusion Processes  18 The KramersMoyal Coefficients of NonStationary Time series in The Presence of Microstructure (Measurement) Noise  19 Influence of Finite Time Step in Estimating of the KramersMoyal Coefficients  20 Distinguishing Diffusive and Jumpy Behaviors in RealWorld Time Series  21 Reconstruction of Langevin and JumpDiffusion Dynamics From Empirical Uni and Bivariate Time Series  22 Applications and Outlook  23 Epileptic Brain Dynamics
 Dimensions
 unknown
 Edition
 1st ed. 2019.
 Extent
 XVIII, 280 p. 41 illus., 22 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783030184728
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783030184728
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783030184728
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