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Stochastic Approximation and Recursive Algorithms and Applications

This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both;

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Stochastic Approximation and Recursive Algorithms and Applications

This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both;

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Simulation and Chaotic Behavior of Alpha-stable Stochastic Processes

Presents new computer methods in approximation, simulation, and visualization for a host of alpha-stable stochastic processes.;

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Simulation and Chaotic Behavior of Alpha-stable Stochastic Processes

Presents new computer methods in approximation, simulation, and visualization for a host of alpha-stable stochastic processes.;

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Approximation

polynomials, splines, and operators, and applications to stochastics; numerical methods for approximation of deterministic and stochastic integrals;

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Stochastic Optimization Methods: Applications in Engineering and Operations Research

book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor;

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Finite Approximations in Discrete-Time Stochastic Control

In a unified form, this monograph presents fundamental results on the approximation of centralized and decentralized stochastic control;

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Numerical Approximations of Stochastic Differential Equations with Non-Globally Lipschitz Continuous Coefficients

Many stochastic differential equations (SDEs) in the literature have a superlinearly growing nonlinearity in their drift or diffusion;

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Approximation Theory XVI

These proceedings are based on the international conference Approximation Theory XVI held on May 19-22, 2019 in Nashville, Tennessee. The;

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Stochastic Systems In Merging Phase Space

This book provides recent results on the stochastic approximation of systems by weak convergence techniques. General and particular schemes;

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Adaptive Stochastic Methods

This monograph develops adaptive stochastic methods in computational mathematics. The authors discuss the basic ideas of the algorithms and;

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Numerical Methods for Stochastic Computations

The@ first graduate-level textbook to focus on fundamental aspects of numerical methods for stochastic computations, this book describes;

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Stochastic Approximation

This simple, compact toolkit for designing and analyzing stochastic approximation algorithms requires only basic literacy in probability;

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American-Type Options

The book gives a systematical presentation of stochastic approximation methods for discrete time Markov price processes. Advanced methods;

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Advances in Linear Stochastic Programming

Multi-stage Stochastic Programming (MSP) has many practical applications for solving problems whose current resolution must be made while;

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Extinction and Quasi-Stationarity in the Stochastic Logistic SIS Model

and from quasi-stationarity for the stochastic logistic SIS model. The approximations are derived separately in three different parameter;

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Stochastic Models of Neural Networks

embrace the approximation of non-random functions as well as the theory of stochastic processes. The networks so defined constitute an original;

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Stochastic Programming

stochastic models discussed. The target audience for this title is graduate students or researchers in optimisation, approximation, statistics;

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Intro To Computational Stochastic PDEs

. Coverage includes traditional stochastic ODEs with white noise forcing, strong and weak approximation, and the multi-level Monte Carlo method;

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Stochastic Integration with Jumps

Stochastic processes with jumps and random measures are importance as drivers in applications like financial mathematics and signal;

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Applied Stochastic Differential Equations

covers the essentials of Ito calculus, the central theorems in the field, and such approximation schemes as stochastic Runge-Kutta. Greater;

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Multidimensional Stochastic Processes As Rough Paths

Rough path analysis provides a fresh perspective on Ito's important theory of stochastic differential equations. Key theorems of modern;

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Stochastic Integration with Jumps

Stochastic processes with jumps and random measures are importance as drivers in applications like financial mathematics and signal;

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An Introduction to Computational Stochastic PDEs

. Coverage includes traditional stochastic ODEs with white noise forcing, strong and weak approximation, and the multi-level Monte Carlo method;

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Advanced Mathematical Tools for Automatic Control Engineers: Volume 2

techniques for dynamic models and filtering, prediction, and smoothing problems. It also discusses the stochastic approximation method and the robust;

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Stochastic Linear Programming Algorithms

. The following methods are considered: regularized decomposition, stochastic decomposition and successive discrete approximation methods for;

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Analysis and Approximation of Rare Events

stochastic systems. A feature of the book is the systematic use of variational representations for quantities of interest such as normalized;

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