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This is a "first course" in the sense that it presumes no previous course in probability. The units are modules taken from the unpublished text: Paul E. Pfeiffer, ELEMENTS OF APPLIED PROBABILITY,USING MATLAB. The units are numbered as they appear in the text, although of course they may be used in any desired order. For those who wish to use the order of the text, an outline isprovided, with indication of which modules contain the material.
The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. Appendix B provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired.
In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which werefer to as m-programs , or simply programs ) to solve many important problems in basic probability. This should make the work useful as a stand alone exposition as well as a supplement to any of severalcurrent textbooks.
Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternateimplementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox,if they desire its aid in evaluating integrals).
Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called minterm analysis . This material is not only important for computational purposes, but is also useful in displaying some ofthe structure of the relationships among events.
Much of "real world" probabilistic thinking is an amalgam of intuitive, plausible reasoning and of statistical knowledge and insight. Mathematical probability attempts to to lend precision to suchprobability analysis by employing a suitable mathematical model , which embodies the central underlying principles and structure. A successful model serves as an aid (and sometimes corrective)to this type of thinking.
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