Bayesian Scientific Computing by Daniela Calvetti, Erkki Some... - 9783031238260
£90.69
Bayesian Scientific ComputingAuthor(s): Daniela Calvetti, Erkki Somersalo\nFormat: Paperback\nPublisher: Springer International Publishing AG, Switzerland\nImprint: Springer International Publishing AG\nISBN-13: 9783031238260, 978-3031238260\nSynopsis\nThe 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.
Bayesian Scientific Computing by Daniela Calvetti, Erkki Some... - 9783031238260
Bayesian Scientific ComputingAuthor(s): Daniela Calvetti, Erkki Somersalo\nFormat: Paperback\nPublisher: Springer International Publishing AG, Switzerland\nImprint: Springer International Publishing AG\nISBN-13: 9783031238260, 978-3031238260\nSynopsis\nThe 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.
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