Paul E. Pfeiffer - Introduction to Applied Probability
Here you can read online Paul E. Pfeiffer - Introduction to Applied Probability full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. publisher: Connexions, genre: Romance novel. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:
Romance novel
Science fiction
Adventure
Detective
Science
History
Home and family
Prose
Art
Politics
Computer
Non-fiction
Religion
Business
Children
Humor
Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.
- Book:Introduction to Applied Probability
- Author:
- Publisher:Connexions
- Genre:
- Rating:5 / 5
- Favourites:Add to favourites
- Your mark:
Introduction to Applied Probability: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Introduction to Applied Probability" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
Paul E. Pfeiffer: author's other books
Who wrote Introduction to Applied Probability? Find out the surname, the name of the author of the book and a list of all author's works by series.
Introduction to Applied Probability — read online for free the complete book (whole text) full work
Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Introduction to Applied Probability" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.
Font size:
Interval:
Bookmark:
Applied Probability
By: Paul Pfeiffer
Online:
This selection and arrangement of content as a collection is copyrighted by Paul Pfeiffer .
It is licensed under the Creative Commons Attribution License: http://creativecommons.org/licenses/by/3.0/
Collection structure revised: 2009/08/31
For copyright and attribution information for the modules contained in this collection, see the "" section at the end of the collection.
Preface to Pfeiffer Applied Probability
1. The course
This is a "first course" in the sense that it presumes no previous course in probability. The units aremodules 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 maybe 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. 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 mathematicalmodel, 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 beenrevised 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 basicMATLAB 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 necessaryto provide some supplementary analytical material, principally the so-called minterm analysis . Thismaterial 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 ofstatistical knowledge and insight. Mathematical probability attempts to to lend precision to suchprobability analysis by employing a suitable mathematical model , which embodies the centralunderlying principles and structure. A successful model serves as an aid (and sometimes corrective)to this type of thinking.
Certain concepts and patterns have emerged from experience and intuition. The mathematicalformulation (the mathematical model) which has most successfully captured these essential ideas is rootedin measure theory, and is known as the Kolmogorov model , after the brilliant Russianmathematician A.N. Kolmogorov (1903-1987).
One cannot prove that a model is correct . Only experience may show whether it is useful (andnot incorrect). The usefulness of the Kolmogorov model is establishedby examining its structure and showing that patterns of uncertainty and likelihood inany practical situation can be represented adequately. Developments, such as in this course, havegiven ample evidence of such usefulness.
The most fruitful approach is characterized by an interplay of
A formulation of the problem in precise terms of the model and careful mathematical analysisof the problem so formulated.
A grasp of the problem based on experience and insight. This underlies both problemformulation and interpretation of analytical results of the model. Often such insight suggestsapproaches to the analytical solution process.
In this work, we make extensive use of MATLAB as an aid to analysis.I have tried to write the MATLAB programs in such a way that they constitute useful,ready-made tools for problem solving. Once the user understands the problems theyare designed to solve, the solution strategies used, and the manner in which thesestrategies are implemented, the collection of programs should provide a usefulresource.
Font size:
Interval:
Bookmark:
Similar books «Introduction to Applied Probability»
Look at similar books to Introduction to Applied Probability. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.
Discussion, reviews of the book Introduction to Applied Probability and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.