000			03868nam a22004935i 4500
001			978-0-387-73394-4
003			DE-He213
005			20250710084017.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387733944 _a99780387733944
024	7		_a10.1007/978-0-387-73394-4 _2doi
082	0	4	_a004 _223
100	1		_aCalvetti, Daniela. _eauthor.
245	1	0	_aIntroduction to Bayesian Scientific Computing _h[recurso electrónico] : _bTen Lectures on Subjective Computing / _cby Daniela Calvetti, Erkki Somersalo.
264		1	_aNew York, NY : _bSpringer New York, _c2007.
300			_aXIV, 202p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSurveys and Tutorials in the Applied Mathematical Sciences ; _v2
505	0		_aInverse problems and subjective computing -- Basic problem of statistical inference -- The praise of ignorance: randomness as lack of information -- Basic problem in numerical linear algebra -- Sampling: first encounter -- Statistically inspired preconditioners -- Conditional Gaussian densities and predictive envelopes -- More applications of the Gaussian conditioning -- Sampling: the real thing -- Wrapping up: hypermodels, dynamic priorconditioners and Bayesian learning.
520			_aA combination of the concepts subjective - or Bayesian - statistics and scientific computing, the book provides an integrated view across numerical linear algebra and computational statistics. Inverse problems act as the bridge between these two fields where the goal is to estimate an unknown parameter that is not directly observable by using measured data and a mathematical model linking the observed and the unknown. Inverse problems are closely related to statistical inference problems, where the observations are used to infer on an underlying probability distribution. This connection between statistical inference and inverse problems is a central topic of the book. Inverse problems are typically ill-posed: small uncertainties in data may propagate in huge uncertainties in the estimates of the unknowns. To cope with such problems, efficient regularization techniques are developed in the framework of numerical analysis. The counterpart of regularization in the framework of statistical inference is the use prior information. This observation opens the door to a fruitful interplay between statistics and numerical analysis: the statistical framework provides a rich source of methods that can be used to improve the quality of solutions in numerical analysis, and vice versa, the efficient numerical methods bring computational efficiency to the statistical inference problems. This book is intended as an easily accessible reader for those who need numerical and statistical methods in applied sciences.
650		0	_aMATHEMATICS.
650		0	_aCOMPUTER SCIENCE _xMATHEMATICS.
650		0	_aCOMPUTER SCIENCE.
650		0	_aDISTRIBUTION (PROBABILITY THEORY).
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aMATHEMATICS.
650	2	4	_aCOMPUTATIONAL SCIENCE AND ENGINEERING.
650	2	4	_aSTATISTICS AND COMPUTING/STATISTICS PROGRAMS.
650	2	4	_aCOMPUTATIONAL MATHEMATICS AND NUMERICAL ANALYSIS.
650	2	4	_aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
700	1		_aSomersalo, Erkki. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387733937
830		0	_aSurveys and Tutorials in the Applied Mathematical Sciences ; _v2
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-73394-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58469 _d58469