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Stationary Stochastic Processes

Intended for a second course in stationary processes, Stationary Stochastic Processes: Theory and Applications presents the theory behind;

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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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Hilbert And Banach Space-valued Stochastic Processes

of infinite-dimensional stationary and nonstationary stochastic processes or time series, based on Hilbert and Banach space-valued second order random;

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Introduction to the Theory of Random Processes

, stationary processes, infinitely divisible processes, and Ito stochastic equations. Basics of discrete time martingales are also presented and then;

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Stationary Stochastic Processes for Scientists and Engineers

course, Stationary Stochastic Processes for Scientists and Engineers teaches students how to use these processes efficiently. Carefully balancing;

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Stochastic Processes

Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced;

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Elements of Applied Stochastic Processes

This 3rd edition of the successful Elements of Applied Stochastic Processes improves on the last edition by condensing the material and;

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Student's t-Distribution and Related Stochastic Processes

-dimensional, strictly stationary diffusions with the Student's t-marginal distribution are defined as the unique weak solution for the stochastic;

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Generated Dynamics of Markov and Quantum Processes

generated by one-step stochastic operators. The characteristic feature of quantum processes are oscillations, interference, lots of stationary;

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Estimation of Stochastic Processes with Missing Observations

values of stationary stochastic processes. Estimates are based on observations of the processes with additive stationary noise process. The aim;

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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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A First Course in Stochastic Processes

with random sums, stationary stochastic processes, and diffusion theory. ;

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Advanced Signal Processing and Digital Noise Reduction

of stochastic signal processing. The chapter begins with an introduction to random signals, stochastic processes, probabilistic models and statistical;

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Applied Probability and Stochastic Processes

processes, and spectral analysis of stationary stochastic processes Comprehensive numerical discussions of examples, which replace the more;

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Applied Probability and Stochastic Processes

processes, and spectral analysis of stationary stochastic processes Comprehensive numerical discussions of examples, which replace the more;

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Stochastic Modeling and Analysis of Telecom Networks

as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability;

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Real Options Valuation

often non-stationary and exhibit non-normally distributed returns. Subsequently, more realistic stochastic volatility, jump diffusion, and Levy;

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Stationary Stochastic Processes. (MN-8)

Encompassing both introductory and more advanced research material, these notes deal with the author's contributions to stochastic;

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Stationary Stochastic Processes. (MN-8)

Encompassing both introductory and more advanced research material, these notes deal with the author's contributions to stochastic;

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Stochastic Optimal Control of Structures

by engineering excitations in the nature of non-stationary and non-Gaussian processes. Further, it establishes the theory of and methods for;

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Stochastic Optimal Control of Structures

by engineering excitations in the nature of non-stationary and non-Gaussian processes. Further, it establishes the theory of and methods for;

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Markov Processes for Stochastic Modeling

parameters. A Markov chain is a stochastic process characterized by the Markov prop erty that the distribution of future depends only on the current;

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Stochastic Processes

Based on a highly popular, well-established course taught by the authors, Stochastic Processes: An Introduction, Second Edition discusses;

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Methods of Structural Safety

-contained presentation of extreme-value theory and stochastic processes; stationary, evolutionary, and nonlinear aspects of stochastic response;

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Information Theory Meets Power Laws - Stochastic Processes and Language Models

! Information Theory Meets Power Laws: Stochastic Processes and Language Models presents readers with a novel subtype of a probabilistic approach to;

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Stable Non Gaussian Self Similar Processes with Stationary Increments

sections in the book review random variables, stochastic processes, and integrals, moving on to rigidity and flows, and finally ending with mixed;

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