Wentworth
Institute of Technology
Bayesian Scientific Computing
The 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 insider’s view of how to combine two mature fields, scien...
Saved in:
| Main Authors: | , |
|---|---|
| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
|
| Edition: | 1st ed. 2023. |
| Series: | Applied Mathematical Sciences,
215 |
| Subjects: | |
| Online Access: |
Full text (Wentworth users only) |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | w2958625 | ||
| 005 | 20230509194352.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 230309s2023 sz | s |||| 0|eng d | ||
| 020 | |a 9783031238246 |9 978-3-031-23824-6 | ||
| 024 | 7 | |a 10.1007/978-3-031-23824-6 |2 doi | |
| 035 | |a (DE-He213)978-3-031-23824-6 | ||
| 049 | |a WENN | ||
| 050 | 4 | |a QA71-90 | |
| 072 | 7 | |a PBKS |2 bicssc | |
| 072 | 7 | |a COM014000 |2 bisacsh | |
| 072 | 7 | |a PBKS |2 thema | |
| 082 | 0 | 4 | |a 003.3 |2 23 |
| 100 | 1 | |a Calvetti, Daniela. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Bayesian Scientific Computing |h [electronic resource] / |c by Daniela Calvetti, Erkki Somersalo. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2023. | |
| 300 | |a XVII, 286 p. 77 illus., 55 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Applied Mathematical Sciences, |x 2196-968X ; |v 215 | |
| 505 | 0 | |a Inverse 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 -- . | |
| 520 | |a The 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 insider’s 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. | ||
| 650 | 0 | |a Mathematics—Data processing. | |
| 650 | 0 | |a Algebras, Linear. | |
| 650 | 0 | |a Computer science—Mathematics. | |
| 700 | 1 | |a Somersalo, Erkki. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031238239 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031238253 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031238260 |
| 830 | 0 | |a Applied Mathematical Sciences, |x 2196-968X ; |v 215 | |
| 951 | |a 2958625 | ||
| 999 | f | f | |i 1a149393-8671-5db2-84e0-05c25522eef5 |s 8ab2710d-2a69-5dcd-9bd1-0c3db197aa6c |t 0 |
| 952 | f | f | |a Wentworth Institute of Technology |b Main Campus |c Wentworth Library |d Ebooks |t 0 |e Springer |h Other scheme |
| 856 | 4 | 0 | |t 0 |u https://ezproxywit.flo.org/login?qurl=https://doi.org/10.1007/978-3-031-23824-6 |y Full text (Wentworth users only) |