Identifiability and Regression Analysis of Biological Systems Models: Statistical and Mathematical Foundations and R Scripts, 2nd Edition

Identifiability and Regression Analysis of Biological Systems Models: Statistical and Mathematical Foundations and R Scripts, 2nd Edition, 2, 2024 is a comprehensive biology reference published by Springer. Description By Paola Lecca This richly illustrated book presents the latest techniques for the identifiability analysis and standard and robust regression analysis of complex dynamical models, and looks at their objectives.

Key Features

- This richly illustrated book presents the latest techniques for the identifiability analysis and standard and robust regression analysis of complex dynamical models, and looks at…
- It begins by providing a definition of complexity in dynamic systems, introducing the concepts of system size, density of interactions, stiff dynamics, and the hybrid nature of…
- The discussion then turns to the mathematical foundations of model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best…
- Although the featured examples mainly focus on applications to biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as…
- Readers will learn how to determine identifiability conditions, how to search for an identifiable model, and how to conduct their own regression analysis and diagnostics without…
- This new edition includes a concise, yet comprehensive treatment of the main artificial intelligence methods which can be used for parameter inference in models of complex dynamic…
- It emphasizes the most efficient solutions for generating synthetic data that augment the training data and which are indispensable for machine learning procedures

Product Details

Publisher: Springer
Edition: 2
Published Year: 2024
Format: Publisher PDF
File Size: 6.2 MB
ISBN: 978-3031747472, 9783031747489