| 期刊全称 | Bayesian Scientific Computing | | 影响因子2023 | Daniela Calvetti,Erkki Somersalo | | 视频video | | | 发行地址 | Provides accessible exposition.Presents work of internationally known authors.Includes supplementary material | | 学科分类 | Applied Mathematical Sciences | | 图书封面 |  | | 影响因子 | .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 ti | | Pindex | Book 2023 |
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发表于 2025-3-21 19:01:48
