Stationary Stochastic Processes for Scientists and Engineers

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A01=Georg Lindgren
A01=Holger Rootzen
A01=Maria Sandsten
advanced stationary process modeling
Age Group_Uncategorized
Age Group_Uncategorized
Ak Cos
And Garch Models
Ar
Arma
Author_Georg Lindgren
Author_Holger Rootzen
Author_Maria Sandsten
automatic-update
behavior of stochastic processes in linear filters
Category1=Non-Fiction
Category=PBT
Coherence Spectrum
COP=United States
covariance and spectral estimation
Covariance Function
Cumulative Distribution Function
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Difference Between Fourier Analysis Of Data And Fourier Transformation Of A Covariance Function
discrete-time auto-regressive and moving average processes
Efficient Monte Carlo Simulation
eq_isMigrated=2
eq_nobargain
Frequency Function
Gaussian Process
Hanning Window
Hilbert Transform
Homogeneous Poisson Process
Inhomogeneous Poisson Process
Language_English
Linear Filter
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MATLAB simulation methods
Monte Carlo simulations of stochastic processes
Ornstein Uhlenbeck Process
PA=Available
Poisson Process
Price_€50 to €100
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Random Telegraph Signal
Relationship Between A Covariance Function And Spectral Density
Sea Surface Height
signal processing applications
softlaunch
solution of linear stochastic differential equations
Spatial Poisson Process
spectral analysis techniques
Spectral Density
Spectrum Estimate
Stationary Gaussian Process
Stationary Stochastic Processes
statistical signal analysis
stochastic differential equations
Stochastic Processes In Science And Engineering
time series modeling
Understanding The Mechanisms That Generate Stationary Stochastic Processes
Uniform Conditional
Wiener Process
Yule Walker Equation

Product details

  • ISBN 9781466586185
  • Weight: 620g
  • Dimensions: 156 x 234mm
  • Publication Date: 11 Oct 2013
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Stochastic processes are indispensable tools for development and research in signal and image processing, automatic control, oceanography, structural reliability, environmetrics, climatology, econometrics, and many other areas of science and engineering. Suitable for a one-semester course, Stationary Stochastic Processes for Scientists and Engineers teaches students how to use these processes efficiently. Carefully balancing mathematical rigor and ease of exposition, the book provides students with a sufficient understanding of the theory and a practical appreciation of how it is used in real-life situations. Special emphasis is on the interpretation of various statistical models and concepts as well as the types of questions statistical analysis can answer.

The text first introduces numerous examples from signal processing, economics, and general natural sciences and technology. It then covers the estimation of mean value and covariance functions, properties of stationary Poisson processes, Fourier analysis of the covariance function (spectral analysis), and the Gaussian distribution. The book also focuses on input-output relations in linear filters, describes discrete-time auto-regressive and moving average processes, and explains how to solve linear stochastic differential equations. It concludes with frequency analysis and estimation of spectral densities.

With a focus on model building and interpreting the statistical concepts, this classroom-tested book conveys a broad understanding of the mechanisms that generate stationary stochastic processes. By combining theory and applications, the text gives students a well-rounded introduction to these processes. To enable hands-on practice, MATLAB® code is available online.

Georg Lindgren, Holger Rootzen, Maria Sandsten

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