Includes bibliographical references (p. 156-162) and index.
|Statement||Vivek S. Borkar.|
|LC Classifications||QA274.2 .B67 2008|
|The Physical Object|
|Pagination||ix, 164 p. ;|
|Number of Pages||164|
|LC Control Number||2009285122|
Estimating unknown parameters based on observation data conta- ing information about the parameters is ubiquitous in diverse areas of both theory and application. For example, in system identification. This monograph addresses the problem of "real-time" curve fitting in the presence of noise, from the computational and statistical viewpoints. It examines the problem of nonlinear regression, where observations are made on a time series whose mean-value function is known except for a vector parameter. In contrast to the traditional formulation, data are imagined to arrive in temporal succession. Stochastic Approximation and NonLinear Regression Book Abstract: This monograph addresses the problem of "real-time" curve fitting in the presence of noise, from the computational and statistical viewpoints. It examines the problem of nonlinear regression, where observations are made on a time series whose mean-value function is known except. About this book Introduction This revised and expanded second edition presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems.
"This is the second edition of an excellent book on stochastic approximation, recursive algorithms and applications . Although the structure of the book has not been changed, the authors have thoroughly revised it and added additional material ." (Evelyn Buckwar, Zentralblatt MATH, Vol. , ). Outline •Stochastic gradient descent (stochastic approximation) •Convergence analysis •Reducing variance via iterate averaging Stochastic gradient methods File Size: 1MB. Plan I History and modern formulation of stochastic approximation theory I In-depth look at stochastic gradient descent (SGD) I Introduction to key ideas in stochastic approximation theory such as Lyapunov functions, quasimartingales, and also numerical solutions to di erential equations. 1 of Stochastic approximation: invited paper Lai, Tze Leung, Annals of Statistics, Mean value theorems for stochastic integrals Krylov, N. V., Annals of Probability, Approximations of stochastic partial differential equations Di Nunno, Giulia and Zhang, Tusheng, Annals of Applied Probability,
*immediately available upon purchase as print book shipments may be delayed due to the COVID crisis. ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook : Hindustan Book Agency. This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged.3/5(1). Stochastic approximation is a technique for studying the properties of an experimental situation; it has important applications in fields such as medicine and engineering. Dr Wasan gives a rigorous mathematical treatment of the subject. The main topic of this book is optimization problems involving uncertain parameters, for which stochastic models are available. Although many ways have been proposed to.