This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, ...
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This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field.
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Add this copy of Stochastic Approximation: a Dynamical Systems Viewpoint to cart. $87.54, very good condition, Sold by BetterBookDeals rated 4.0 out of 5 stars, ships from NIAGARA FALLS, NY, UNITED STATES, published 2008 by Cambridge University Press.
Add this copy of Stochastic Approximation: a Dynamical Systems Viewpoint to cart. $89.04, new condition, Sold by BetterBookDeals rated 4.0 out of 5 stars, ships from NIAGARA FALLS, NY, UNITED STATES, published 2008 by Cambridge University Press.
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2009, Hindustan Book Agency and Indian National Science Academy
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