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Carte Bayesian Scientific Computing Daniela Calvetti

Bayesian Scientific Computing

Autor: Daniela Calvetti, Erkki Somersalo
Limbă: engleză
Legare: Copertă tare
Editura: Springer, Berlin
Disponibilitate: În depozitul extern
Expediem în 10-13 zile
716.48 lei
The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly alon...

Informații despre carte

Autor
Daniela Calvetti, Erkki Somersalo
Limbă
engleză
Legare
Carte - Copertă tare
Publicat
2023
Pagini
282
EAN
9783031238239
Enbook ID
42197399
Editura
Springer, Berlin
Greutate
602
Dimensiuni
155 x 235

Descriere completă

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.

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