Stochastic Population Processes
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Stochastic Population Processes
A reference text presenting stochastic processes and a range of approximation and simulation techniques for extracting behavioural information in the context of stochastic population dynamics.
Stochastic Models in Biology
This monograph, first published in 1974, is an attempt to demonstrate the usefulness of the theory of stochastic processes in understanding biologic phenomena at various levels of complexity - from the molecular to the ecologic level. The modeling of biologic systems via stochastic processes allows the incorporation of effects of secondary factors for which a detailed knowledge is missing. In the first two chapters of the monograph, the authors present the mathematic analysis used in the later chapters. The authors attempted to make the chapters self-contained and to make the book comprehensive, bringing in results derived by different authors using a variety of techniques and notations. In later chapters, where models of various biologic phenomena are discussed, introductory reviews of those phenomena are given for readers with less biologic background. Nira Dyn is a professor of Applied Mathematics at Tel-Aviv University in Israel since 1984. Her main field of activity now is Geometric Modeling. Her previous research interests were in Approximation Theory and in Mathematical Modeling of Biological systems. She is now serving in the editorial boards of the Journal of Approximation Theory and of the journal Computer Aided Geometric Design. She wrote more than 130 papers, and participated actively in more than 80 conferences and workshops. Her education was in Applied Mathematics in Israeli institutions; B.Sc from the Technion, Haifa, in 1965, M.Sc from the Weizmann Institute, Rehovot, in 1967, Ph.D. from the Weizmann Institute, Rehovot in 1970.
Stochastic Processes in Genetics and Evolution
Prologue; Acknowledgments; Contents; 1. An Introduction to Mathematical Probability with Applications in Mendelian Genetics; 1.1 Introduction; 1.2 Mathematical Probability in Mendelian Genetics; 1.3 Examples of Finite Probability Spaces; Example 1.3.1: An Equal Frequency Model; Example 1.3.2: Partitions of an Abstract Set; Example 1.3.3: A Deterministic Case; Example 1.3.4: Inheritance of Eye Color and Sex; 1.4 Elementary Combinatorial Analysis; 1.5 The Binomial Distribution; Example 1.5.1: Distribution of Boys and Girls in Families of Size N.