A Comparison Of The L0 Norm And Least Squares For Analysing Data With Possible Outliers

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Numerical methods for scientists and engineers

This book presents an exhaustive and in-depth exposition of the various numerical methods used in scientific and engineering computations. It emphasises the practical aspects of numerical computation and discusses various techniques in sufficient detail to enable their implementation in solving a wide range of problems. The main addition in the third edition is a new Chapter on Statistical Inferences. There is also some addition and editing in the next chapter on Approximations. With this addition 12 new programs have also been added.
Geophysical Data Analysis and Inverse Theory with MATLAB® and Python

Geophysical Data Analysis and Inverse Theory with MATLAB or Python, Fifth Edition is a revised and expanded introduction to inverse theory and tomography as it is practiced by geophysicists. The book demonstrates the methods needed to analyze a broad spectrum of geophysical datasets, with special attention given to those methods that generate images of the earth. Data analysis can be a mathematically complex activity, but the treatment in this volume is carefully designed to emphasize those mathematical techniques that readers will find the most familiar and to systematically introduce less-familiar ones. A series of "crib sheets" offer step-by-step summaries of methods presented.Utilizing problems and case studies, along with MATLAB and Python computer code and summaries of methods, the book provides professional geophysicists, students, data scientists and engineers in geophysics with the tools necessary to understand and apply mathematical techniques and inverse theory. - Includes material on probability, including Bayesian influence, probability density function, and metropolis algorithm - Offers detailed discussions of the application of inverse theory to seismological, gravitational, and tectonic studies - Provides numerous examples, color figures, and end-of-chapter problems to help readers explore and further understand the presented ideas - Includes both MATLAB and Python examples and problem sets