Overview
- Editors:
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James H. Matis
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Department of Statistics, Texas A&M University, College Station, USA
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Thomas R. Kiffe
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Department of Mathematics, Texas A&M University, College Station, USA
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Table of contents (14 chapters)
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Introduction
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- James H. Matis, Thomas R. Kiffe
Pages 2-4
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- James H. Matis, Thomas R. Kiffe
Pages 5-15
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Models for a Single Population
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- James H. Matis, Thomas R. Kiffe
Pages 17-29
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- James H. Matis, Thomas R. Kiffe
Pages 30-39
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- James H. Matis, Thomas R. Kiffe
Pages 40-48
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- James H. Matis, Thomas R. Kiffe
Pages 49-71
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Models for Multiple Populations
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Front Matter
Pages 100-100
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- James H. Matis, Thomas R. Kiffe
Pages 72-99
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- James H. Matis, Thomas R. Kiffe
Pages 101-109
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- James H. Matis, Thomas R. Kiffe
Pages 110-118
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- James H. Matis, Thomas R. Kiffe
Pages 119-136
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- James H. Matis, Thomas R. Kiffe
Pages 137-141
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- James H. Matis, Thomas R. Kiffe
Pages 142-160
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- James H. Matis, Thomas R. Kiffe
Pages 161-171
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- James H. Matis, Thomas R. Kiffe
Pages 172-188
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Back Matter
Pages 189-204
About this book
This monograph has been heavily influenced by two books. One is Ren shaw's [82] work on modeling biological populations in space and time. It was published as we were busily engaged in modeling African bee dispersal, and provided strong affirmation for the stochastic basis for our ecological modeling efforts. The other is the third edition of Jacquez' [28] classic book on compartmental analysis. He reviews stochastic compartmental analysis and utilizes generating functions in this edition to derive many useful re sults. We interpreted Jacquez' use of generating functions as a message that the day had come for modeling practioners to consider using this powerful approach as a model-building tool. We were inspired by the idea of using generating functions and related methods for two purposes. The first is to integrate seamlessly our previous research centering in stochastic com partmental modeling with our more recent research focusing on stochastic population modeling. The second, related purpose is to present some key research results of practical application in a natural, user-friendly way to the large user communities of compartmental and biological population modelers. One general goal of this monograph is to make a case for the practical utility of the various stochastic population models. In accordance with this objective, we have chosen to illustrate the various stochastic models, using four primary applications described in Chapter 2. In so doing, this mono graph is based largely on our own published work.
Reviews
“Models are illustrated with numerous practical applications. … The reviewer enjoyed reading this book which will be immensely useful to mathematicians and statisticians interested in biological modelling. Due to the attractive topics as well as the informal and vivid style often used in the presentation and description of the challenging biological problems, they will find this book absorbing and will benefit a lot from it. It is a valuable addition to the existing line of books on stochastic population models.” (P.R.Parthasarathy, zbMATH 0943.92029, 2022)
Editors and Affiliations
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Department of Statistics, Texas A&M University, College Station, USA
James H. Matis
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Department of Mathematics, Texas A&M University, College Station, USA
Thomas R. Kiffe
