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It would be difficult to overestimate the importance of stochastic independence in both the theoretical development and the practical appli cations of mathematical probability.

It would be difficult to overestimate the importance of stochastic independence in both the theoretical development and the practical appli cations of mathematical probability.

The purpose of this monograph is to provide a concise introduction to the theory of generalized inverses of matrices that is accessible to undergraduate mathematics majors. Although results from this active area of research have appeared in a number of excellent graduate level text­ books since 1971, material for use at the undergraduate level remains fragmented. The basic ideas are so fundamental, however, that they can be used to unify various topics that an undergraduate has seen but perhaps not related. Material in this monograph was first assembled by the author as lecture notes for the senior seminar in mathematics at the University of Tennessee. In this seminar one meeting per week was for a lecture on the subject matter, and another meeting was to permit students to present solutions to exercises. Two major problems were encountered the first quarter the seminar was given. These were that some of the students had had only the required one-quarter course in matrix theory and were not sufficiently familiar with eigenvalues, eigenvectors and related concepts, and that many -v- of the exercises required fortitude. At the suggestion of the UMAP Editor, the approach in the present monograph is (1) to develop the material in terms of full rank factoriza­ tions and to relegate all discussions using eigenvalues and eigenvectors to exercises, and (2) to include an appendix of hints for exercises.

The text of this monograph represents the author's lecture notes from a course taught in the Department of Applied Mathematics and Statistics at the State University of New York at Stony Brook in the Spring of 1977. On account of its origin as lecture notes, some sections of the text are telegraphic in style while other portions are overly detailed. This stylistic foible has not been modified as it does not appear to detract seriously from the readability and it does help to indicate which topics were stressed. The audience for the course at Stony Brook was composed almost entirely of fourth year undergraduates majoring in the mathematical sciences. All of these students had studied at least four semesters of calculus and one of probability; few had any prior experience with either differential equations or ecology. It seems prudent to point out that the author's background is in engineering and applied mathematics and not in the biological sciences. It is hoped that this is not painfully obvious. -vii- The focus of the monograph is on the formulation and solution of mathematical models; it makes no pretense of being a text in ecology. The idea of a population is employed mainly as a pedagogic tool, providing unity and intuitive appeal to the varied mathematical ideas introduced. If the biological setting is stripped away, what remains can be interpreted as topics on the qualitative behavior of differential and difference equations.

The material discussed in this monograph should be accessible to upper level undergraduates in the mathemati­ cal sciences. Formal prerequisites include a solid intro­ duction to calculus and one semester of probability. Although differential equations are employed, these are all linear, constant coefficient, ordinary differential equa­ tions which are solved either by separation of variables or by introduction of an integrating factor. These techniques can be taught in a few minutes to students who have studied calculus. The models developed to describe an epidemic outbreak of smallpox are standard stochastic processes (birth-death, random walk and branching processes). While it would be helpful for students to have seen these prior to their introduction in this monograph, it is certainly not necessary. The stochastic processes are developed from first principles and then solved using elementary tech­ niques. Since all that turns out to be necessary are ex­ pected values of random variables, the differential-differ­ ence equatlon descriptions of the stochastic processes are reduced to ordinary differential equations before being solved. Students who have studied stochastic processes are generally pleased to learn that different formulations are possible for the same set of conditions. The choice of which formulation to employ depends upon what one wishes to calculate. Specifically, in Section 6 a birth-death pro­ cess is replaced by a random walk and in Section 7 a prob­ lem is formulated both as a multi-birth-death process and as a branching process.