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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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Monte-carlo Methods and Stochastic Processes

focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear;

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Monte-Carlo Methods and Stochastic Processes

focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear;

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Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology

of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic;

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Simulation of Stochastic Processes with Given Accuracy and Reliability

the new approaches and modern methods of simulation of stochastic processes, this book provides methods and tools in measuring accuracy and;

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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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Foundations and Methods of Stochastic Simulation

This graduate-level textbook covers modelling, programming and analysis of stochastic computer simulation experiments, including the;

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Foundations and Methods of Stochastic Simulation

This graduate-level textbook covers modelling, programming and analysis of stochastic computer simulation experiments, including the;

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Stochastic Processes: Modeling and Simulation

This sequel to volume 19 of Handbook on Statistics on Stochastic Processes: Modelling and Simulation is concerned mainly with the theme;

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Introduction to Stochastic Analysis

is paid to simulation diffusion processes. The topics covered include Brownian motion; motivation of stochastic models with Brownian motion; Ito;

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Numerical Methods For Stochastic Processes

new methods. Topics presented include Monte Carlo and quasi-Monte Carlo methods, the simulation of major stochastic processes and;

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Stochastic Simulation Optimization

With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering;

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Simulation and Inference for Stochastic Processes with Yuima: A Comprehensive R Framework for Sdes and Other Stochastic Processes

The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic;

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Stochastic Simulation Optimization For Discrete Event Systems

millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a hard nut to crack;

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Simulation and Inference for Stochastic Differential Equations

. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers;

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Simulation of Dynamic Systems with MATLAB (R) and Simulink (R)

Continuous-system simulation is an increasingly important tool for optimizing the performance of real-world systems. The book presents an;

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Simulation of Dynamic Systems with MATLAB (R) and Simulink (R)

Continuous-system simulation is an increasingly important tool for optimizing the performance of real-world systems. The book presents an;

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Comparison Methods for Stochastic Models and Risks

Stochastic order relations prprovide a valuable insight into the behaviour of complex stochastic (random) systems and enable the user to;

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Analytical and Stochastic Modelling Techniques and Applications

from 30 submissions. The papers discuss the latest developments in analytical, numerical and simulation algorithms for stochastic systems;

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Analytical and Stochastic Modelling Techniques and Applications

numerous submissions. The papers discuss the latest developments in analytical, numerical and simulation algorithms for stochastic systems;

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Stochastic Hydrology and Its Use in Water Resources Systems Simulation and Optimization

Stochastic hydrology is an essential base of water resources systems analysis, due to the inherent randomness of the input, and;

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Analytical and Stochastic Modelling Techniques and Applications

from 27 submissions. The scope of the conference is on following topics: analytical, numerical and simulation algorithms for stochastic systems;

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Introduction to Computational Earthquake Engineering

numerical simulation of strong ground motion and faulting, respectively. Stochastic modeling is used for uncertain underground structures, and;

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Numerical Probability

covered include the Monte Carlo simulation (including simulation of random variables, variance reduction, quasi-Monte Carlo simulation, and more;

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Stochastic Modelling For Systems Biology

researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a;

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Recent Development in Stochastic Dynamics and Stochastic Analysis

areas. Stochastic dynamical systems tools for modeling and simulation are highly demanded in investigating complex phenomena in, for example;

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