001461356 000__ 04762cam\a2200637\i\4500
001461356 001__ 1461356
001461356 003__ OCoLC
001461356 005__ 20230503003348.0
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001461356 008__ 230313s2023\\\\sz\a\\\\ob\\\\001\0\eng\d
001461356 019__ $$a1372548480
001461356 020__ $$a9783031238246$$q(electronic bk.)
001461356 020__ $$a3031238249$$q(electronic bk.)
001461356 020__ $$z3031238230
001461356 020__ $$z9783031238239
001461356 0247_ $$a10.1007/978-3-031-23824-6$$2doi
001461356 035__ $$aSP(OCoLC)1372500261
001461356 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dUKAHL$$dOCLCF
001461356 049__ $$aISEA
001461356 050_4 $$aQA371
001461356 08204 $$a515/.357$$223/eng/20230313
001461356 1001_ $$aCalvetti, Daniela,$$eauthor.
001461356 24510 $$aBayesian scientific computing /$$cDaniela Calvetti, Erkki Somersalo.
001461356 264_1 $$aCham :$$bSpringer,$$c[2023]
001461356 264_4 $$c©2023
001461356 300__ $$a1 online resource (xvii, 286 pages) :$$billustrations (some color).
001461356 336__ $$atext$$btxt$$2rdacontent
001461356 337__ $$acomputer$$bc$$2rdamedia
001461356 338__ $$aonline resource$$bcr$$2rdacarrier
001461356 4901_ $$aApplied mathematical sciences ;$$vvolume 215
001461356 504__ $$aIncludes bibliographical references and index.
001461356 5050_ $$aInverse problems and subjective computing -- Linear algebra -- Continuous and discrete multivariate distributions -- Introduction to sampling -- The praise of ignorance: randomness as lack of certainty -- Enter subject: Construction of priors -- Posterior densities, ill-conditioning, and classical regularization -- Conditional Gaussian densities -- Iterative linear solvers and priorconditioners -- Hierarchical models and Bayesian sparsity -- Sampling: the real thing -- Dynamic methods and learning from the past -- Bayesian filtering and Gaussian densities -- .
001461356 506__ $$aAccess limited to authorized users.
001461356 520__ $$aThe once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insiders view of how to combine two mature fields, scientific computing and Bayesian inference, into a powerful language leveraging the capabilities of both components for computational efficiency, high resolution power and uncertainty quantification ability. The impact of Bayesian scientific computing has been particularly significant in the area of computational inverse problems where the data are often scarce or of low quality, but some characteristics of the unknown solution may be available a priori. The ability to combine the flexibility of the Bayesian probabilistic framework with efficient numerical methods has contributed to the popularity of Bayesian inversion, with the prior distribution being the counterpart of classical regularization. However, the interplay between Bayesian inference and numerical analysis is much richer than providing an alternative way to regularize inverse problems, as demonstrated by the discussion of time dependent problems, iterative methods, and sparsity promoting priors in this book. The quantification of uncertainty in computed solutions and model predictions is another area where Bayesian scientific computing plays a critical role. This book demonstrates that Bayesian inference and scientific computing have much more in common than what one may expect, and gradually builds a natural interface between these two areas.
001461356 588__ $$aDescription based on print version record.
001461356 650_0 $$aInverse problems (Differential equations)$$xNumerical solutions.
001461356 650_0 $$aBayesian statistical decision theory.
001461356 655_0 $$aElectronic books.
001461356 7001_ $$aSomersalo, Erkki,$$eauthor.
001461356 77608 $$iPrint version:$$aCALVETTI, DANIELA. SOMERSALO, ERKKI.$$tBAYESIAN SCIENTIFIC COMPUTING.$$d[Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2023$$z3031238230$$w(OCoLC)1355022272
001461356 830_0 $$aApplied mathematical sciences (Springer-Verlag New York Inc.) ;$$vv. 215.
001461356 852__ $$bebk
001461356 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-23824-6$$zOnline Access$$91397441.1
001461356 909CO $$ooai:library.usi.edu:1461356$$pGLOBAL_SET
001461356 980__ $$aBIB
001461356 980__ $$aEBOOK
001461356 982__ $$aEbook
001461356 983__ $$aOnline
001461356 994__ $$a92$$bISE