The Decision Analysis community may be interested in the new electronic version of my introductory probability text. It is a non-traditional text with innovative elements including Excel-based Monte Carlo simulation.
The text uses event trees to give the student visual insight into concepts such as independence, the total probability rule, Bayes' rule, and the binomial and geometric random variables. It uses an intuitive presentation of the influence diagram to schematically portray Bayes rule using arrow reversals and to give intuitive meaning to the notion of conditional independence.
Other decision-analytic tools are also discussed in this text, including sensitivity analysis, tornado diagrams, probabilistic sensitivity analysis, decision trees, and expected utility. See the attached document for details.
This textbook has been used for fifteen years in the required undergraduate probability course in the Department of Industrial Engineering and Management Sciences at Northwestern University. The electronic version has been available as an option the last few quarters. It is not a supplement, summary, or set of lecture notes - it is the complete version of a 591-page text. It is viewable on your computer or electronic device, and can be accessed at a reasonable fee through the website scribd.com via the link Probability an Introduction With Applications. The text is hyperlinked both to and from its table of contents, and also contains crosslinks within its body.
Contents of the text
Preface to the instructor • i
Random Variables, Events, and Probabilities • 1
1 Basic Concepts • 2
2 Conditional Probability and Independence • 53
3 The Mean • 78
4 More on Conditional Probability* • 97
Discrete Random Variables • 162
5 Probability Mass Functions • 163
6 Repeated Independent Trials • 194
7 The Expectation Operator • 232
8 Variance and Covariance • 264
9 More on Conditioning* • 301
Continuous Random Variables • 342
10 Basic Properties of Continuous Random Variables • 343
11 Further Properties of Continuous Random Variables • 370
12 Important Continuous Random Variables • 423
Discrete and Continuous Random Variables • 451
13 Sums of Random Variables • 452
14 The Poisson Process* • 487
15 Overview of Important Discrete and Continuous Random Variables • 501
Further Topics • 527
16 Applications in Statistical Inference* • 528
17 Applications of Monte Carlo Simulation* • 539
18 Classical Versus Bayesian Inference* • 561
Appendix: Discrete Mathematics Requirements • 576
Bibliography • 585
Index • 586
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Gordon Hazen
Professor
Northwestern University
Evanston IL
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