Stochastic Approximation and Recursive Algorithms and Applications (Stochastic Modelling and Applied Probability, 35)
by Harold Kushner, G. George Yin
Stochastic Approximation and Recursive Algorithms and Applications, from the Stochastic Modelling and Applied Probability series, examines key methods in probability and modeling. Harold Kushner and G. George Yin provide a comprehensive look at recursive techniques for handling stochastic processes, ideal for those studying applied mathematics and optimization in uncertain systems.
About This Book
Stochastic Approximation and Recursive Algorithms and Applications is part of the Stochastic Modelling and Applied Probability series, volume 35. Authored by Harold Kushner and G. George Yin, it delves into the foundational concepts of stochastic approximation methods.
The text covers recursive algorithms and their practical implementations, providing a structured approach to handling uncertainty in probabilistic models. It emphasizes theoretical underpinnings while addressing real-world applications.
Readers will find detailed explorations of algorithms designed for optimization and estimation in stochastic environments. The book builds on established principles to offer clarity on complex recursive processes.
As a specialized volume, it contributes to the broader field of applied probability, making it valuable for researchers and practitioners in related disciplines.
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