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The Inner Workings of Life
Computer Age Statistical Inference
Vignettes in Systems Biology Eberhard O. Voit
Algorithms, Evidence, and Data Science Bradley Efron
Georgia Institute of Technology
Comprised of short vignettes, this introductory text explains key concepts within a systems biology approach without resorting to mathematical equations or technical jargon. Suitable for students and researchers alike, this is the first book aimed at a more introductory level to systems biology. ‘Popular science books have enabled the public to gain an appreciation for advances in a variety of esoteric disciplines ranging from chaos theory to evolutionary biology. But we lack a ‘fun read’ for the emerging field of systems biology, which is bringing together computer scientists, physicists and biologists to figure out the complex inner workings of living cells. Eberhard O. Voit has filled that gap with his new book. Voit provides the reader with an insider’s tour of systems biology, providing us [with] a sense of how this exciting field will change our lives in the coming years. Impress and challenge your book club with this new offering.’ James J. Collins, Massachusetts Institute of Technology and Harvard University, Massachusetts 2016 228 x 152 mm 222pp 978-1-107-14995-3 Hardback £49.99 / US$79.99 978-1-316-60442-7 Paperback £19.99 / US$29.99
Stanford University, California
and Trevor Hastie Stanford University, California
Computing power has revolutionized the theory and practice of statistical inference. This book delivers a concentrated course in modern statistical thinking by tracking the revolution from classical theories to the large-scale prediction algorithms of today. Anyone who applies statistical methods to data will benefit from this landmark text. ‘How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read, Efron and Hastie, two pioneers in the integration of parametric and nonparametric statistical ideas, give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear, historically informed examples.’ Andrew Gelman, Columbia University, New York
Contents: Part I. Classic Statistical Inference; Part II. Early Computer-Age Methods; Part III. Twenty-First Century Topics. Institute of Mathematical Statistics Monographs, 5
For all formats available, see
2016 228 x 152 mm 492pp 5 b/w illus. 40 colour illus. 50 tables 978-1-107-14989-2 Hardback £45.99 / US$74.99
www.cambridge.org/9781107149953
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www.cambridge.org/9781107149892
eBooks available at www.cambridge.org/ebookstore