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Numerical and Statistical Methods for Bioengineering
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  • Cited by 11
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    This (lowercase (translateProductType product.productType)) has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Rodriguez-Moreno, F. Kren, J. Zemek, F. Novak, J. Lukas, V. and Pikl, M. 2017. Advantage of multispectral imaging with sub-centimeter resolution in precision agriculture: generalization of training for supervised classification. Precision Agriculture, Vol. 18, Issue. 4, p. 615.

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    Tripathi, Amit K. and Sundberg, Donald C. 2015. A Hybrid Algorithm for Accurate and Efficient Monte Carlo Simulations of Free-Radical Polymerization Reactions. Macromolecular Theory and Simulations, Vol. 24, Issue. 1, p. 52.

    Khan, Mohammad Nasim Imtiaz Khan, Md. Zadid Noor, Dewan Fahim Nahiyan, Adib Haque, Md. Ehsanul Saha, Shammya Shananda Rachi, Md Rifat Kaisar and Khan, Md. Ziaur Rahman 2014. Modelling and simulation of an efficient Charge Controller for Photovoltaic System with Maximum Power Point Tracking. p. 1.

    Kraning-Rush, Casey M. and Reinhart-King, Cynthia A. 2012. Controlling matrix stiffness and topography for the study of tumor cell migration. Cell Adhesion & Migration, Vol. 6, Issue. 3, p. 274.

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Book description

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.

Reviews

'I think this book is a winner … [it] is really easy to read and places frameworks for numerical analysis into realistic bioengineering concepts that students will find familiar and relevant. This is most evident in the excellent boxed examples, but also in many of the homework problems. I also really liked the 'key points to consider' at the end of the chapters - these are useful reminders for the students. Finally, the book presents bioinformatics in a manageable fashion that should help demystify this subject for interested students.'

K. Jane Grande-Allen - Rice University

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