9789813274600-9813274603-Stochastic Models in the Life Sciences and Their Methods of Analysis

Stochastic Models in the Life Sciences and Their Methods of Analysis

ISBN-13: 9789813274600
ISBN-10: 9813274603
Edition: 1
Author: Frederic Y M Wan
Publication date: 2019
Publisher: World Scientific Publishing Co
Format: Hardcover 476 pages
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Book details

ISBN-13: 9789813274600
ISBN-10: 9813274603
Edition: 1
Author: Frederic Y M Wan
Publication date: 2019
Publisher: World Scientific Publishing Co
Format: Hardcover 476 pages

Summary

Acknowledged author Frederic Y M Wan wrote Stochastic Models in the Life Sciences and Their Methods of Analysis comprising 476 pages back in 2019. Textbook and eTextbook are published under ISBN 9813274603 and 9789813274600. Since then Stochastic Models in the Life Sciences and Their Methods of Analysis textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

Biological processes are evolutionary in nature and often evolve in a noisy environment or in the presence of uncertainty. Such evolving phenomena are necessarily modeled mathematically by stochastic differential/difference equations (SDE), which have been recognized as essential for a true understanding of many biological phenomena. Yet, there is a dearth of teaching material in this area for interested students and researchers, notwithstanding the addition of some recent texts on stochastic modelling in the life sciences. The reason may well be the demanding mathematical pre-requisites needed to "solve" SDE. A principal goal of this volume is to provide a working knowledge of SDE based on the premise that familiarity with the basic elements of a stochastic calculus for random processes is unavoidable. Through some SDE models of familiar biological phenomena, we show how stochastic methods developed for other areas of science and engineering are also useful in the life sciences. In the process, the volume introduces to biologists a collection of analytical and computational methods for research and applications in this emerging area of life science. The additions broaden the available tools for SDE models for biologists that have been limited by and large to stochastic simulations.

Readership: Undergraduates, graduates, research students, professionals with interests in mathematical biology, stochastic processes and infectious diseases.

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