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Author: A. V. Balakrishnan Publisher: ISBN: Category : Languages : en Pages : 53
Book Description
This paper presents a theory of estimation of parameters in linear stochastic differential equations based on time-continuous observation. It uses a white noise model to represent observation errors (in contrast to a Wiener process model). The application is to the problem of identifying aircraft as well as turbulence (wind-gust) parameters from flight test data. Results obtained on actual data are presented.
Author: A. V. Balakrishnan Publisher: ISBN: Category : Languages : en Pages : 53
Book Description
This paper presents a theory of estimation of parameters in linear stochastic differential equations based on time-continuous observation. It uses a white noise model to represent observation errors (in contrast to a Wiener process model). The application is to the problem of identifying aircraft as well as turbulence (wind-gust) parameters from flight test data. Results obtained on actual data are presented.
Author: Jaya P. N. Bishwal Publisher: Springer ISBN: 3540744487 Category : Mathematics Languages : en Pages : 271
Book Description
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
Author: Rong SITU Publisher: Springer Science & Business Media ISBN: 0387251758 Category : Technology & Engineering Languages : en Pages : 444
Book Description
Stochastic differential equations (SDEs) are a powerful tool in science, mathematics, economics and finance. This book will help the reader to master the basic theory and learn some applications of SDEs. In particular, the reader will be provided with the backward SDE technique for use in research when considering financial problems in the market, and with the reflecting SDE technique to enable study of optimal stochastic population control problems. These two techniques are powerful and efficient, and can also be applied to research in many other problems in nature, science and elsewhere.
Author: Peter H. Baxendale Publisher: World Scientific ISBN: 9812706623 Category : Science Languages : en Pages : 416
Book Description
The first paper in the volume, Stochastic Evolution Equations by N V Krylov and B L Rozovskii, was originally published in Russian in 1979. After more than a quarter-century, this paper remains a standard reference in the field of stochastic partial differential equations (SPDEs) and continues to attract attention of mathematicians of all generations, because, together with a short but thorough introduction to SPDEs, it presents a number of optimal and essentially non-improvable results about solvability for a large class of both linear and non-linear equations.
Author: Jaya P. N. Bishwal Publisher: Springer Nature ISBN: 3031038614 Category : Mathematics Languages : en Pages : 634
Book Description
This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
Author: S. Ōmatu Publisher: Oxford University Press, USA ISBN: Category : Mathematics Languages : en Pages : 456
Book Description
In this unified account of the mathematical theory of distributed parameter systems (DPS), the authors cover all major aspects of the control, estimation, and identification of such systems, and their application in engineering problems. The first part of the book is devoted to the basic results in deterministic and stochastic partial differential equations, which are applied to the optimal control and estimation theories for DPS. Part two then applies this knowledge in an engineering setting, discussing optimal estimators, optimal sensor and actuator locations, and computational techniques.
Author: Pavel S. Knopov Publisher: Springer Science & Business Media ISBN: 1461482860 Category : Mathematics Languages : en Pages : 191
Book Description
Focusing on research surrounding aspects of insufficiently studied problems of estimation and optimal control of random fields, this book exposes some important aspects of those fields for systems modeled by stochastic partial differential equations. It contains many results of interest to specialists in both the theory of random fields and optimal control theory who use modern mathematical tools for resolving specific applied problems, and presents research that has not previously been covered. More generally, this book is intended for scientists, graduate, and post-graduates specializing in probability theory and mathematical statistics. The models presented describe many processes in turbulence theory, fluid mechanics, hydrology, astronomy, and meteorology, and are widely used in pattern recognition theory and parameter identification of stochastic systems. Therefore, this book may also be useful to applied mathematicians who use probability and statistical methods in the selection of useful signals subject to noise, hypothesis distinguishing, distributed parameter systems optimal control, and more. Material presented in this monograph can be used for education courses on the estimation and control theory of random fields.
Author: A. V. Balakrishnan
Author: A. V. Balakrishnan
Author: Jaya P. N. Bishwal
Author: Rong SITU
Author: Simo Särkkä
Author: Yoshikazu Sawaragi
Author: Peter H. Baxendale
Author: Andrew P. Sage
Author: Jaya P. N. Bishwal
Author: S. Ōmatu
Author: Pavel S. Knopov