Uncertainty Quantification using R

This book provides a rigorous yet practical introduction to uncertainty quantification techniques,with applications in R and Python. It covers linear and nonlinear programming,Lagrange multipliers,multi-objective optimization,game theory,linear algebraic equations,and probability and statistics,making it accessible to students and professionals.

Format: Hardback
Length: 766 pages
Publication date: 23 February 2023
Publisher: Springer International Publishing AG

This comprehensive book offers a rigorous yet practical exploration of uncertainty quantification techniques, with applications in both R and Python. It delves into mathematical arguments at a level that ensures rigorousness while maintaining a clear focus on practical applications of these methods. Additionally, practical aspects of applied probability are addressed, making the content accessible to students. By introducing R and Python, the book enables readers to tackle more complex problems involving a greater number of variables. Examples provided throughout the text facilitate the solution of medium-sized problems.

The topics covered in this volume are diverse and include linear and nonlinear programming, Lagrange multipliers for sensitivity analysis, multi-objective optimization, game theory, linear algebraic equations, and probability and statistics. This book seamlessly blends theoretical rigor with practical applications, making it of interest to professionals, researchers, graduate, and undergraduate students engaged in the utilization of uncertainty quantification techniques within the realm of operations research and mathematical programming. Its applications in management and planning make it an invaluable resource for those seeking to enhance their understanding and expertise in this field.

Weight: 1322g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031177842
Edition number: 1st ed. 2023