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Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques
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Don Hong 作者
World Scientific Publishing Company
譯者
2007-07-10 出版日期
353 頁數
USD 148.00 價格
Hardcover
叢書系列
9789812704610 圖書編碼

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Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


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Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques 在線電子書 著者簡介

Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques 在線電子書 著者簡介

Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques 在線電子書 圖書描述

Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techniques and mathematical models using differential equations, new developments with a very broad spectrum of applications, such as wavelets, spline functions, curve and surface subdivisions, sampling, and learning theory, have found their mathematical home in biomedical data analysis. This book gives a new and integrated introduction to quantitative medical data analysis from the viewpoint of biomathematicians, biostatisticians, and bioinformaticians. It offers a definitive resource to bridge the disciplines of mathematics, statistics, and biomedical sciences. Topics include mathematical models for cancer invasion and clinical sciences, data mining techniques and subset selection in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic and proteomic data analysis, wavelet and spline applications for mass spectrometry data preprocessing, statistical computing, and graphics for dynamic molecules.

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