£73.10

Springer - Uncertainty Quantification and Predictive Science Book

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Description

Bridge the knowledge gap in computational simulation with this comprehensive textbook from Springer. Designed specifically for physical scientists and engineers, this book provides the essential background needed to understand and quantify uncertainties within computational models. As simulation becomes more common in high-consequence decision-making, having the skills to predict system behavior under uncertainty is more important than ever. The text builds a strong foundation by starting with fundamental building blocks. Readers will review core concepts including probability, sampling, and Bayesian statistics before moving into more advanced topics. By constructing sophisticated prediction techniques from these basics, the book helps professionals master the skills required for accurate predictive computational science. This resource is an important tool for anyone working to improve the reliability of simulations in engineering and the physical sciences.

Key Features

/uploads/images/products/2/B0D3V33VCR/e0ca738c-7cb9-4eaf-b3bd-a50300958ddc.webp

Builds a strong mathematical foundation by reviewing probability, sampling, and Bayesian statistics.

Teaches how to predict the behavior of complex systems when operating under various uncertainties.

Designed specifically as a foundational resource for physical scientists and engineers.

Constructs advanced prediction techniques using basic building blocks for a step-by-step learning approach.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
05 December 2018
Listed Since
17 July 2018

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