# Stochastic Systems

## Uncertainty Quantification and Propagation

- 21 Citations
- 22k Downloads

Part of the Springer Series in Reliability Engineering book series (RELIABILITY)

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- 21 Citations
- 22k Downloads

Part of the Springer Series in Reliability Engineering book series (RELIABILITY)

Uncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of *Stochastic Systems* is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives *Stochastic Systems *presents:

· A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis

· * *Probabilistic models for random variables and functions needed to formulate stochastic equations describing realistic problems in engineering and applied sciences

· * *Practical methods for quantifying the uncertain parameters in the definition of stochastic equations, solving approximately these equations, and assessing the accuracy of approximate solutions

*Stochastic Systems *provides key information for researchers, graduate students, and engineers who are interested in the formulation and solution of stochastic problems encountered in a broad range of disciplines. Numerous examples are used to clarify and illustrate theoretical concepts and methods for solving stochastic equations. The extensive bibliography and index at the end of the book constitute an ideal resource for both theoreticians and practitioners.

Monte Carlo Simulation Probabilistic Models Random Functions Stochastic Equations Stochastic Systems

- DOI https://doi.org/10.1007/978-1-4471-2327-9
- Copyright Information Springer-Verlag London Limited 2012
- Publisher Name Springer, London
- eBook Packages Engineering Engineering (R0)
- Print ISBN 978-1-4471-2326-2
- Online ISBN 978-1-4471-2327-9
- Series Print ISSN 1614-7839
- Buy this book on publisher's site