Bayesian Scientific Computing

This book combines scientific computing and Bayesian inference to create a powerful language for computational efficiency, high resolution power, and uncertainty quantification. It has significantly impacted computational inverse problems, where data are scarce or of low quality, and has demonstrated the ability to combine the flexibility of the Bayesian probabilistic framework with efficient numerical methods. It also quantifies uncertainty in computed solutions and model predictions.

Format: Hardback
Length: 286 pages
Publication date: 10 March 2023
Publisher: Springer International Publishing AG

The integration of scientific computing with probabilistic frameworks, particularly in the Bayesian paradigm, has gained widespread popularity and has found applications in various fields. This book offers an insightful perspective on combining these two mature domains, scientific computing, and Bayesian inference, to create a powerful language that harnesses the strengths of both components for enhanced computational efficiency, high resolution power, and uncertainty quantification capabilities.

The impact of Bayesian scientific computing has been particularly significant in the realm of computational inverse problems, where data availability is often limited or of low quality, yet certain characteristics of the unknown solution may be known a priori. By leveraging the flexibility of the Bayesian probabilistic framework with efficient numerical methods, Bayesian inversion has gained popularity, with the prior distribution serving as a counterpart to classical regularization techniques. However, the interplay between Bayesian inference and numerical analysis extends far beyond simply providing an alternative approach to regularizing inverse problems. This book explores various aspects, including time-dependent problems, iterative methods, and sparsity-promoting priors, highlighting the rich and intricate connections between these two fields.

Furthermore, Bayesian scientific computing plays a crucial role in the quantification of uncertainty in computed solutions and model predictions. This book demonstrates that Bayesian inference and scientific computing share more commonalities than initially perceived, gradually building a natural interface between these two areas. By integrating these two disciplines, researchers and practitioners can gain a deeper understanding of complex systems and make informed decisions based on rigorous statistical analysis.

In conclusion, this book provides a comprehensive and accessible guide to combining scientific computing and Bayesian inference, offering valuable insights and techniques for addressing challenging problems in diverse fields. It demonstrates the power of integrating these two domains and highlights their potential to revolutionize the way we approach scientific inquiry and data analysis.

Weight: 623g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031238239
Edition number: 1st ed. 2023