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Information theory and the central limit theorem / Oliver Johnson
New Jersey : Imperial College Press, 2004
xiv, 209 pages. : illustrations ; 24 cm.
Ký hiệu phân loại (DDC): 519.2
This book provides a comprehensive description of a new method of proving the central limit theorem, through the use of apparently unrelated results from information theory. It gives a basic introduction to the concepts of entropy and Fisher information, and collects together standard results concerning their behaviour. It brings together results from a number of research papers as well as unpublished material, showing how the techniques can give a unified view of limit theorems
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Introductory biological statistics / John Edward Havel, Raymond E. Hampton, Scott J. Meiners
Long Grove, Illinois : Waveland Press, Inc., 2019
x, 252 pages. : illustrations ; 28 cm.
Ký hiệu phân loại (DDC): 570.1
A thorough understanding of biology, no matter which subfield, requires a thorough understanding of statistics. As in previous editions, Havel and Hampton (with new co-author Scott Meiners) ground students in all essential methods of descriptive and inferential statistics, using examples from different biological sciences. The authors have retained the readable, accessible writing style popular with both students and instructors. Pedagogical improvements new to this edition include concept checks in all chapters to assist students in active learning and code samples showing how to solve many of the book’s examples using R. Each chapter features numerous practice and homework exercises, with larger data sets available for download.
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