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Stochastic Approximation A Dynamical Systems Viewpoint Borkar Hardback

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Product Description

Stochastic Approximation

A Dynamical Systems Viewpoint

Simple, compact toolkit for designing and analyzing algorithms, with concrete examples from control and communications engineering, artificial intelligence, economic modelling.

Vivek S. Borkar (Author)

9780521515924, Cambridge University Press

Hardback, published 1 September 2008

176 pages

23.3 x 15.7 x 1.7 cm, 0.38 kg

'This simple compact toolkit for designing and analyzing stochastic approximation algorithms requires only basic literacy in probability and differential equations \u2026 Ideal for graduate students, researchers and practitioners in electrical engineering and computer science, especially those working in control, communications, signal processing and machine learning, this book is also relevant to economics, probability and statistics.' L'Enseignement Math\u00E9matique

This simple, compact toolkit for designing and analyzing stochastic approximation algorithms requires only basic literacy i]

Stochastic Approximation A Dynamical Systems Viewpoint Borkar Hardback

Stochastic Approximation

A Dynamical Systems Viewpoint

Simple, compact toolkit for designing and analyzing algorithms, with concrete examples from control and communications engineering, artificial intelligence, economic modelling.

Vivek S. Borkar (Author)

9780521515924, Cambridge University Press

Hardback, published 1 September 2008

176 pages

23.3 x 15.7 x 1.7 cm, 0.38 kg

'This simple compact toolkit for designing and analyzing stochastic approximation algorithms requires only basic literacy in probability and differential equations \u2026 Ideal for graduate students, researchers and practitioners in electrical engineering and computer science, especially those working in control, communications, signal processing and machine learning, this book is also relevant to economics, probability and statistics.' L'Enseignement Math\u00E9matique

This simple, compact toolkit for designing and analyzing stochastic approximation algorithms requires only basic literacy i]

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