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001			287482
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008			150903s2012 xxk| o |||| 0|eng d
020			_a9781447123279 _99781447123279
024	7		_a10.1007/9781447123279 _2doi
035			_avtls000339557
039		9	_a201509030839 _bVLOAD _c201404300402 _dVLOAD _y201402060938 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTA169.7
100	1		_aGrigoriu, Mircea. _eautor _9316684
245	1	0	_aStochastic Systems : _bUncertainty Quantification and Propagation / _cby Mircea Grigoriu.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2012.
300			_axI, 529 páginas 136 ilustraciones, 61 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringer Series in Reliability Engineering, _x1614-7839
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781447123262
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-2327-9 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c287482 _d287482