000			03964nam a22005055i 4500
001			978-1-4471-2327-9
003			DE-He213
005			20140220083235.0
007			cr nn 008mamaa
008			120514s2012 xxk| s |||| 0|eng d
020			_a9781447123279 _9978-1-4471-2327-9
024	7		_a10.1007/978-1-4471-2327-9 _2doi
050		4	_aTA169.7
050		4	_aT55-T55.3
050		4	_aTA403.6
072		7	_aTGPR _2bicssc
072		7	_aTEC032000 _2bisacsh
082	0	4	_a658.56 _223
100	1		_aGrigoriu, Mircea. _eauthor.
245	1	0	_aStochastic Systems _h[electronic resource] : _bUncertainty Quantification and Propagation / _cby Mircea Grigoriu.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2012.
300			_aXI, 529 p. 136 illus., 61 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Reliability Engineering, _x1614-7839
505	0		_aProbability Essentials -- Random Functions -- Probabilistic Models -- Stochastic Integrals and Itô's Formula -- Properties of Solutions of Stochastic Equations -- Stochastic Equations with Small Uncertainty -- Stochastic Algebraic Equations -- Stochastic Differential Equations with Deterministic Coefficients -- Stochastic Differential Equations with Random Coefficients.
520			_aUncertainty 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.
650		0	_aEngineering.
650		0	_aDistribution (Probability theory).
650		0	_aEngineering mathematics.
650		0	_aSystem safety.
650	1	4	_aEngineering.
650	2	4	_aQuality Control, Reliability, Safety and Risk.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aAppl.Mathematics/Computational Methods of Engineering.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781447123262
830		0	_aSpringer Series in Reliability Engineering, _x1614-7839
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-2327-9
912			_aZDB-2-ENG
999			_c100651 _d100651