000			03291nam a22004815i 4500
001			978-0-387-76896-0
003			DE-He213
005			20250710084025.0
007			cr nn 008mamaa
008			130821s2009 xxu| s |||| 0|eng d
020			_a9780387768960 _a99780387768960
024	7		_a10.1007/978-0-387-76896-0 _2doi
082	0	4	_a519.2 _223
100	1		_aBain, Alan. _eauthor.
245	1	0	_aFundamentals of Stochastic Filtering _h[recurso electrónico] / _cby Alan Bain, Dan Crisan.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2009.
300			_aXIII, 390 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStochastic Modelling and Applied Probability, _x0172-4568 ; _v60
505	0		_aFiltering Theory -- The Stochastic Process ? -- The Filtering Equations -- Uniqueness of the Solution to the Zakai and the Kushner-Stratonovich Equations -- The Robust Representation Formula -- Finite-Dimensional Filters -- The Density of the Conditional Distribution of the Signal -- Numerical Algorithms -- Numerical Methods for Solving the Filtering Problem -- A Continuous Time Particle Filter -- Particle Filters in Discrete Time.
520			_aThe objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient background to enable study of the recent literature. While no prior knowledge of stochastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are required are stated and proved in the appendices. The book is intended as a reference for graduate students and researchers interested in the field. It is also suitable for use as a text for a graduate level course on stochastic filtering. Suitable exercises and solutions are included.
650		0	_aMATHEMATICS.
650		0	_aFINANCE.
650		0	_aNUMERICAL ANALYSIS.
650		0	_aDISTRIBUTION (PROBABILITY THEORY).
650	1	4	_aMATHEMATICS.
650	2	4	_aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
650	2	4	_aCONTROL, ROBOTICS, MECHATRONICS.
650	2	4	_aNUMERICAL ANALYSIS.
650	2	4	_aQUANTITATIVE FINANCE.
700	1		_aCrisan, Dan. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387768953
830		0	_aStochastic Modelling and Applied Probability, _x0172-4568 ; _v60
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-76896-0 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58845 _d58845