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Approximation & Weak Convergence Methods For Random Processes With Applications

detailed development of approximation and limit theorems and methods for random processes and applies them to numerous problems of practical;

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Weak Convergence of Stochastic Processes

range of literature, are for the first time presented here in a self-contained fashion. Contents: Weak convergence of stochastic processes;

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On Stein's Method for Infinitely Divisible Laws with Finite First Moment

This book focuses on quantitative approximation results for weak limit theorems when the target limiting law is infinitely divisible with;

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Extreme Value Methods with Applications to Finance

Methods with Applications to Finance concentrates on modern topics in EVT, such as processes of exceedances, compound Poisson approximation;

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Approximate Iterative Algorithms

functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error;

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Approximate Iterative Algorithms

functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error;

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Stochastic Geometry, Spatial Statistics and Random Fields: Asymptotic Methods

is focused on asymptotic methods in geometric probability including weak and strong limit theorems for random spatial structures (point processes;

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Probability and Random Processes

of random variables, computer generation of random variates, random processes and stationarity concepts with associated autocovariance and cross;

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Elementary Applications of Probability Theory

the weak law of numbers. The next four chapters introduce random processes, including random walks and Markov chains illustrated by examples;

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Elementary Applications of Probability Theory

the weak law of numbers. The next four chapters introduce random processes, including random walks and Markov chains illustrated by examples;

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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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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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Asymptotic Analyses for Complex Evolutionary Systems with Markov and Semi-Markov Switching Using Approximation Schemes

and the control of processes and their generators. Weak convergence of stochastic processes is usually proved by verifying two conditions;

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Stochastic Limit Theory

strong laws of large numbers; weak convergence; and central limit theorems for nonstationary and dependent processes. The functional central;

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Renewal Theory for Perturbed Random Walks and Similar Processes

of their distributions and the existence of moments, as well as weak convergence of divergent perpetuities. Focusing on random processes with;

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Optimization Under Stochastic Uncertainty

. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective;

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Optimization Under Stochastic Uncertainty

. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective;

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The Finite Element Method for Boundary Value Problems

is presented as a method of approximation for differential operators that are mathematically classified as self-adjoint, non-self-adjoint, and non;

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Weak Convergence And Its Applications

Weak convergence of stochastic processes is one of most important theories in probability theory. Not only probability experts but also;

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Stochastic Optimization Methods: Applications in Engineering and Operations Research

solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute;

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Functional Integrals

approximate computation methods of functional integrals on a wide class of measures, including measures generated by homogeneous random processes with;

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Probability And Stochastic Processes

for random variables and processes: Bernoulli, multinomial, exponential, Gamma, Beta, Dirichlet, Poisson, Gaussian, Chi2, ordered variables;

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Probability And Stochastic Processes

for random variables and processes: Bernoulli, multinomial, exponential, Gamma, Beta, Dirichlet, Poisson, Gaussian, Chi2, ordered variables;

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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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Functional Gaussian Approximation for Dependent Structures

taken. Firstly, the authors present tools for dealing with the dependent structures used to obtain normal approximations. Secondly, they apply;

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Random Motions in Markov and SemiMarkov Random Environments 1

homogenous random motions. This volume consists of two parts, the first describing the basic concepts and methods that have been developed for random;

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Large Deviations and Idempotent Probability

deviation principle (LDP) and well-known methods for establishing weak convergence results. Large Deviations and Idempotent Probability expounds;

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