Applied Probability
Paul Pfeiffer
Applied Probability
Free
Description
Contents
Reviews

The present collection utilizes a number of user defined m-programs, in combination with built in MATLAB functions, for solving a variety of probabilistic problems. These m-files are included as text files in the collection New Prob m-files. We use the term m-function to designate a user-defined function as distinct from the basic MATLAB functions which are part of the MATLAB package. An m-procedure (or sometimes a procedure) is an m-file containing a set of MATLAB commands which carry out a prescribed set of operations. Generally, these will prompt for (or assume) certain data upon which the procedure is carried out. We use the term m-program (or often m-file) to refer to either an m-function or an m-procedure. Although most of the m-programs were written for MATLAB version 4.2, they work for versions 5.1, 5.2, and 7.04. The latter versions offer some new features which may make more efficient implementation of some of the m-programs, and which make possible some new ones. With one exception (so noted), these are not exploited in this collection, because of the pedagogical value of dealing with explicitly developed procedures whose dependence on basic MATLAB is displayed. These programs, with perhaps some exceptions, also run on the MATLAB alternatives SCILAB and OCTAVE. Users of these latter programs should be able to make appropriate adjustments if needed. In addition to the m-programs there is a collection of m-files for specific problems with properly formatted data which can be entered into the workspace by calling the file. These m-files come from a variety of sources ( e.g., exams or problem sets, hence the odd names) and may be useful for examples and exercises. This collection is in the text file New Prob mfiles.

Language
English
ISBN
1512672734
Applied Probability
Preface to Pfeiffer Applied Probability
1. Probability Systems
2. Minterm Analysis
3. Conditional Probability
4. Independence of Events
5. Conditional Independence
6. Random Variables and Probabilities
7. Distribution and Density Functions
8. Random Vectors and joint Distributions
9. Independent Classes of Random Variables
10. Functions of Random Variables
11. Mathematical Expectation
12. Variance, Covariance, Linear Regression
13. Transform Methods
14. Conditional Expectation, Regression
15. Random Selection
16. Conditional Independence, Given a Random Vector
17. Appendices
Index
Attributions
About Connexions
The book hasn't received reviews yet.