Stochastic processes in information and dynamical systems.
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Stochastic processes in information and dynamical systems.

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Published by McGraw-Hill in New York .
Written in English


  • Systems engineering.,
  • Stochastic processes.

Book details:

Edition Notes

SeriesMcGraw-Hill series in systems science
LC ClassificationsTA168 .W65
The Physical Object
Paginationxii, 308 p.
Number of Pages308
ID Numbers
Open LibraryOL5703252M
ISBN 100070715688
LC Control Number70139568

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This book is a revision of Stochastic Processes in Information and Dynamical Systems written by the first author (E.W.) and published in The book was originally written, and revised, to provide a graduate level text in stochastic processes for students whose primary interest is its applications. SIAM Review. Article Tools. Add to my favorites. Download Citations. Track Citations. Keyword: Isometric Arnoldi Process (1) Keyword: Minimum Norm Solution (2) Stochastic Processes in Information and Dynamical Systems (Eugene Wong) Related Databases. Web of ScienceAuthor: R. E. Mortensen. This book presents the general theory and basic methods of linear and nonlinear stochastic systems (StS) i.e. dynamical systems described by stochastic finite- and infinite-dimensional differential, integral, integrodifferential, difference etc equations. The general StS theory is based on the equations for characteristic functions and by: Such models naturally render to statistical description, where the input parameters and solutions are expressed by random processes and fields. The fundamental problem of stochastic dynamics is to identify the essential characteristics of system (its state and evolution), and relate those to the input parameters of the system and initial data.

Stochastic dynamical systems are dynamical systems subjected to the effect of noise. The randomness brought by the noise takes into account the variability observed in real-world phenomena. For example, the evolution of a share price typically exhibits long-term behaviors along with faster, smaller-amplitude oscillations, reflecting day-to-day. Biography of I.I. Gikhman. Iosif Ilyich Gikhman was born on the 26 th of May in the city of Uman, Ukraine. He studied in Kiev, graduating in , then remained there to teach and do research under the supervision of N. Bogolyubov, defending a "candidate" thesis on the influence of random processes on dynamical systems in and a doctoral dissertation on Markov processes and. Dynamical properties of random walks Ali Messaoudi and Glauco Valle Scaling limit for escapes from unstable equilibria in the vanishing noise limit: Nontrivial Jordan block case Yuri Bakhtin and Zsolt Pajor-Gyulai Self-normalized asymptotic properties for the parameter estimation in fractional Ornstein-Uhlenbeck process.   The theory of stochastic processes originally grew out of efforts to describe Brownian motion quantitatively. Today it provides a huge arsenal of methods suitable for analyzing the influence of noise on a wide range of systems. The credit for acquiring all the deep insights and powerful methods is due ma- ly to a handful of physicists and mathematicians: Einstein, Smoluchowski, Langevin.

  This book introduces some advanced topics in probability theories — both pure and applied — is divided into two parts. The first part deals with the analysis of stochastic dynamical systems, in terms of Gaussian processes, white noise theory, and diffusion processes. " Stochastic Processes in Information and Dynamical Systems by Eugene Wong A readable copy. All pages are intact, and the cover is intact. Pages can include considerable notes-in pen or highlighter-but the notes cannot obscure the text. An ex-library book and . Additional Physical Format: Online version: Wong, Eugene, Stochastic processes in information and dynamical systems. New York, McGraw-Hill []. Dynamical Systems and Stochastic Processes. P. Collet Centre de Physique Th´eorique CNRS UMR Ecole Polytechnique F Palaiseau Cedex (France) e-mail: [email protected] Ap Abstract This series of lectures is devoted to the study of the statistical proper-ties of dynamical systems. When equipped with an invariant.