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An introduction to stochastic processes with applications to biology

By: Allen, Linda J. S.
Material type: materialTypeLabelBookPublisher: Boca Raton, FL : Chapman & Hall/CRC, ©2011Edition: 2nd ed.Description: xxiv, 466 p. : ill. ; 25 cm.ISBN: 9781439818824.Subject(s): Stochastic processes | Biomathematics
Contents:
Mean First Passage Time -- An Example: Genetics Inbreeding Problem -- Unrestricted Random Walk in Higher Dimensions -- Two Dimensions --
Logistic Growth Process -- Quasistationary Probability Distribution -- SIS Epidemic Model -- Deterministic Model -- Stochastic Model -- Chain Binomial Epidemic Models --
Summary: "The second edition of a bestseller, this textbook delineates stochastic processes, emphasizing applications in biology. It includes MATLAB throughout the book to help with the solutions of various problems. The book is organized according to the three types of stochastic processes: discrete time Markov chains, continuous time Markov chains and continuous time and state Markov processes. It contains a new chapter on the biological applications of stochastic differential equations and new sections on alternative methods for derivation of a stochastic differential equation, data and parameter estimation, Monte Carlo simulation, and more"--
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Mathematics REF 519.23 ALL-I (Browse shelf) Available 004466
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Mean First Passage Time -- An Example: Genetics Inbreeding Problem -- Unrestricted Random Walk in Higher Dimensions -- 2.10.1. Two Dimensions --

3.7. Logistic Growth Process -- 3.8. Quasistationary Probability Distribution -- 3.9. SIS Epidemic Model -- 3.9.1. Deterministic Model -- 3.9.2. Stochastic Model -- 3.10. Chain Binomial Epidemic Models --

"The second edition of a bestseller, this textbook delineates stochastic processes, emphasizing applications in biology. It includes MATLAB throughout the book to help with the solutions of various problems. The book is organized according to the three types of stochastic processes: discrete time Markov chains, continuous time Markov chains and continuous time and state Markov processes. It contains a new chapter on the biological applications of stochastic differential equations and new sections on alternative methods for derivation of a stochastic differential equation, data and parameter estimation, Monte Carlo simulation, and more"--

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