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Estimates of Stochastic Processes with Stationary Increments and Cointegrated Sequences

processing. This book gives a deep understanding of spectral theory and estimation techniques for stochastic processes with stationary increments. It;

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Modern Trends in Controlled Stochastic Processes:

methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information;

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Discrete Stochastic Processes and Optimal Filtering

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from;

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Discrete Stochastic Processes and Optimal Filtering

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from;

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Nonlinear Stochastic Control and Filtering with Engineering-Oriented Complexities

Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for;

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Nonlinear Stochastic Control and Filtering With Engineering-Oriented Complexities

Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for;

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Performance Analysis and Synthesis for Discrete-Time Stochastic Systems with Network-Enhanced Complexities

provides a unified treatment on the analysis and synthesis for discrete-time stochastic systems with guarantee of certain performances against;

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Performance Analysis and Synthesis for Discrete-Time Stochastic Systems with Network-Enhanced Complexities

provides a unified treatment on the analysis and synthesis for discrete-time stochastic systems with guarantee of certain performances against;

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Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems

appreciation of the text: a basic introduction to stochastic control processes, aspects of linear matrix inequality optimization, and MATLAB codes for;

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Adaptive Filtering Prediction and Control

estimation, and adaptive prediction and control. The second part examines stochastic systems, exploring optimal filtering and prediction, parameter;

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Stochastic Control and Filtering over Constrained Communication Networks

on stochastic control and filtering for networked systems under constrained communication networks. It provides a framework of optimal;

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Variance-Constrained Multi-Objective Stochastic Control and Filtering

This title unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented;

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Nonlinear Stochastic Systems with Incomplete Information

Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem;

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Sequential Stochastic Optimization

presents a unified theory of optimal stopping and optimal sequential control of stochastic processes. This book has been carefully organized so;

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Markov Processes and Applications

, Professor at Universite de Paris-Orsay, France. Markov processes is the class of stochastic processes whose past and future are conditionally;

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Advanced Mathematical Tools for Automatic Control Engineers: Volume 2

time processes, including the concepts of random sequences, martingales, and limit theorems. Part III covers continuous time stochastic;

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Stochastic Processes & Filtering Theory

text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting;

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Fundamentals of Stochastic Filtering

assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are;

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Fundamentals of Stochastic Filtering

assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are;

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Discrete Stochastic Processes

approaches the subject via many simple examples which build insight into the structure of stochastic processes and the general effect of these;

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Optimal Control of Stochastic Difference Volterra Equations

of optimal control such as meeting given performance criteria, and stabilization, extending them to neutral stochastic difference Volterra equations;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Introduction to Random Signals and Noise

grounding, this text provides a clear introduction to the fundamentals of stochastic processes and their practical applications to random signals;

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Optimal Filtering

This graduate-level text augments and extends beyond undergraduate studies of signal processing, particularly in regard to;

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Stochastic Dynamics, Filtering and Optimization

introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes;

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Control and System Theory of Discrete-Time Stochastic Systems

discrete-time stochastic systems so that they can then apply its principles to their own stochastic control systems and to the solution of control;

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