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Sequential Stochastic Optimization

Sequential Stochastic Optimization provides mathematicians and applied researchers with a well-developed framework in which stochastic;

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Fast Sequential Monte Carlo Methods for Counting and Optimization

for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for;

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Portfolio Optimization with Different Information Flow

Portfolio Optimization with Different Information Flow recalls the stochastic tools and results concerning the stochastic;

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Adaptive Stochastic Optimization Techniques with Applications

Adaptive Stochastic Optimization Techniques with Applications provides a single, convenient source for state-of-the-art information on;

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Stochastic Global Optimization

optimization has been receiving considerable attention in the past two decades. Of the two types of techniques for global optimization, stochastic;

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Stochastic Optimization with Simulation Based Optimization

Stochastic optimization is vital to making sound engineering and business decisions under uncertainty. While the limited capability;

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Stochastic Process Optimization using Aspen Plus (R)

Stochastic Process Optimization using Aspen (R) Plus Bookshop Category: Chemical Engineering Optimization can be simply;

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Stochastic Process Optimization using Aspen Plus (R)

Stochastic Process Optimization using Aspen (R) Plus Bookshop Category: Chemical Engineering Optimization can be simply defined as;

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Mean Field Simulation for Monte Carlo Integration

chain models, stochastic optimization, nonlinear filtering and multiple target tracking, stochastic optimization, calibration and uncertainty;

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Mean Field Simulation for Monte Carlo Integration

chain models, stochastic optimization, nonlinear filtering and multiple target tracking, stochastic optimization, calibration and uncertainty;

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Stochastic Optimization

Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic;

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Decision Theory

Decision Theory An Introduction to Dynamic Programming and Sequential Decisions John Bather University of Sussex, UK Mathematical induction;

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Stochastic Optimization Methods: Applications in Engineering and Operations Research

differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods;

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Stochastic Analysis and Applications

This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and;

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Stochastic Analysis and Applications

This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and;

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Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes

Stochastic global optimization methods and applications to chemical, biochemical, pharmaceutical and environmental processes presents;

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Differential Evolution In Chemical Engineering

solving optimization problems are of deterministic or stochastic type. Of these, stochastic techniques can solve any type of optimization problems;

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Stochastic Modeling and Optimization

This books covers the broad range of research in stochastic models and optimization. Applications presented include networks, financial;

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Introduction To Stochastic Search And Optimization

A unique interdisciplinary foundation for real-world problem solving Stochastic search and optimization techniques are used in a vast;

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Handbook of Markov Decision Processes

OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic;

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Handbook of Markov Decision Processes

OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic;

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Designing Engineering Structures using Stochastic Optimization Methods

, there are limitations in using traditional methods. As such, stochastic optimization methods such as differential evolution, simulated annealing;

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Stochastic Simulation Optimization For Discrete Event Systems

of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and;

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Optimization

gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient;

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Stochastic Dynamics, Filtering and Optimization

solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB (R) codes;

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Particle Filters for Random Set Models

This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and;

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Global Optimization

This self-contained monograph presents a new stochastic approach to global optimization problems arising in a variety of disciplines;

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