Numerical and Statistical Methods for Bioengineering: Applications in MATLAB

Numerical and Statistical Methods for Bioengineering: Applications in MATLAB

by Michael R. King, Nipa A. Mody
     
 

ISBN-10: 0521871581

ISBN-13: 9780521871587

Pub. Date: 12/31/2010

Publisher: Cambridge University Press

The first MATLAB-based numerical methods textbook for bioengineers that uniquely integrates modelling concepts with statistical analysis, while maintaining a focus on enabling the user to report the error or uncertainty in their result. Between traditional numerical method topics of linear modelling concepts, nonlinear root finding, and numerical integration,

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Overview

The first MATLAB-based numerical methods textbook for bioengineers that uniquely integrates modelling concepts with statistical analysis, while maintaining a focus on enabling the user to report the error or uncertainty in their result. Between traditional numerical method topics of linear modelling concepts, nonlinear root finding, and numerical integration, chapters on hypothesis testing, data regression and probability are interweaved. A unique feature of the book is the inclusion of examples from clinical trials and bioinformatics, which are not found in other numerical methods textbooks for engineers. With a wealth of biomedical engineering examples, case studies on topical biomedical research, and the inclusion of end of chapter problems, this is a perfect core text for a one-semester undergraduate course.

Product Details

ISBN-13:
9780521871587
Publisher:
Cambridge University Press
Publication date:
12/31/2010
Series:
Cambridge Texts in Biomedical Engineering Series
Edition description:
New Edition
Pages:
594
Product dimensions:
7.30(w) x 9.80(h) x 1.30(d)

Table of Contents

1. Types and sources of numerical error; 2. Systems of linear equations; 3. Statistics and probability; 4. Hypothesis testing; 5. Root finding techniques for nonlinear equations; 6. Numerical quadrature; 7. Numerical integration of ordinary differential equations; 8. Nonlinear data regression and optimization; 9. Basic algorithms of bioinformatics; Appendix A. Introduction to MATLAB; Appendix B. Location of nodes for Gauss-Legendre quadrature.

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