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  3. Branching Processes
  • Cited by 262
Publisher:
Cambridge University Press
Online publication date:
May 2010
Print publication year:
2005
Online ISBN:
9780511629136
DOI:
https://doi.org/10.1017/CBO9780511629136
Subjects:
Statistics for Life Sciences, Medicine and Health, Life Sciences, Evolutionary Biology, Statistics and Probability
Series:
Cambridge Studies in Adaptive Dynamics (5)
Information

Book description

Biology takes a special place among the other natural sciences because biological units, be they pieces of DNA, cells or organisms, reproduce more or less faithfully. As for any other biological processes, reproduction has a large random component. The theory of branching processes was developed especially as a mathematical counterpart to this most fundamental of biological processes. This active and rich research area allows us to make predictions about both extinction risks and the development of population composition, and also uncovers aspects of a population's history from its current genetic composition. Branching processes play an increasingly important role in models of genetics, molecular biology, microbiology, ecology and evolutionary theory. This book presents this body of mathematical ideas for a biological audience, but should also be enjoyable to mathematicians.

Reviews

'… the book should serve well, among other things, as an excellent introduction to applied probabilists looking for interesting and worthwhile applications.'

Source: Publication of the International Statistical Institute

'In summary, this is an excellent book with a plethora of real biological examples and lucid writing style that is accessible to most students and scientists with a good command over calculus and basic probability. The book can serve well for a two-semester course in stochastic processes in biology with one semester devoted to branching processes in biology. I am confident this book will soon become a standard reference in the subject. I highly recommend this book to anyone who is interested in branching process models in biological sciences.'

Source: Journal of the American Statistical Association

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