000			03770nam a22005415i 4500
001			978-0-387-27132-3
003			DE-He213
005			20250710083937.0
007			cr nn 008mamaa
008			100301s2005 xxu| s |||| 0|eng d
020			_a9780387271323 _a99780387271323
024	7		_a10.1007/b138659 _2doi
082	0	4	_a518 _223
082	0	4	_a518 _223
100	1		_aKaipio, Jari P. _eauthor.
245	1	0	_aStatistical and Computational Inverse Problems _h[recurso electrónico] / _cby Jari P. Kaipio, Erkki Somersalo.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_aXVI, 339 p. 102 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aApplied Mathematical Sciences, _x0066-5452 ; _v160
505	0		_aInverse Problems and Interpretation of Measurements -- Classical Regularization Methods -- Statistical Inversion Theory -- Nonstationary Inverse Problems -- Classical Methods Revisited -- Model Problems -- Case Studies.
520			_aThe book develops the statistical approach to inverse problems with an emphasis on modeling and computations. The framework is the Bayesian paradigm, where all variables are modeled as random variables, the randomness reflecting the degree of belief of their values, and the solution of the inverse problem is expressed in terms of probability densities. The book discusses in detail the construction of prior models, the measurement noise modeling and Bayesian estimation. Markov Chain Monte Carlo-methods as well as optimization methods are employed to explore the probability distributions. The results and techniques are clarified with classroom examples that are often non-trivial but easy to follow. Besides the simple examples, the book contains previously unpublished research material, where the statistical approach is developed further to treat such problems as discretization errors, and statistical model reduction. Furthermore, the techniques are then applied to a number of real world applications such as limited angle tomography, image deblurring, electrical impedance tomography and biomagnetic inverse problems. The book is intended to researchers and advanced students in applied mathematics, computational physics and engineering. The first part of the book can be used as a text book on advanced inverse problems courses. The authors Jari Kaipio and Erkki Somersalo are Professors in the Applied Physics Department of the University of Kuopio, Finland and the Mathematics Department at the Helsinki University of Technology, Finland, respectively.
650		0	_aMATHEMATICS.
650		0	_aCOMPUTER SCIENCE _xMATHEMATICS.
650		0	_aDISTRIBUTION (PROBABILITY THEORY).
650		0	_aMATHEMATICAL PHYSICS.
650		0	_aSTATISTICS.
650		0	_aBIOMEDICAL ENGINEERING.
650	1	4	_aMATHEMATICS.
650	2	4	_aCOMPUTATIONAL MATHEMATICS AND NUMERICAL ANALYSIS.
650	2	4	_aMATHEMATICAL AND COMPUTATIONAL PHYSICS.
650	2	4	_aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
650	2	4	_aSYSTEMS AND INFORMATION THEORY IN ENGINEERING.
650	2	4	_aBIOMEDICAL ENGINEERING.
650	2	4	_aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY & GEOSCIENCES.
700	1		_aSomersalo, Erkki. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387220734
830		0	_aApplied Mathematical Sciences, _x0066-5452 ; _v160
856	4	0	_uhttp://dx.doi.org/10.1007/b138659 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c56635 _d56635