000			03579nam a2200373 i 4500
001			OTLid0000256
003			MnU
005			20241120064010.0
006			m o d s
007			cr
008			180907s2009 mnu o 0 0 eng d
040			_aMnU _beng _cMnU
050		4	_aQA1
050		4	_aQA37.3
100	1		_aPfeiffer, Paul _eauthor
245	0	0	_aApplied Probability _cPaul Pfeiffer
264		2	_aMinneapolis, MN _bOpen Textbook Library
264		1	_a[Place of publication not identified] _bOpenStax CNX _c[2009]
264		4	_c©2009.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aOpen textbook library.
505	0		_a1 Preface -- 2 Probability Systems -- 3 Minterm Analysis -- 4 Conditional Probability -- 5 Independence of Events -- 6 Conditional Independence -- 7 Random Variables and Probabilities -- 8 Distribution and Density Functions -- 9 Random Vectors and joint Distributions -- 10 Independent Classes of Random Variables -- 11 Functions of Random Variables -- 12 Mathematical Expectation -- 13 Variance, Covariance, Linear Regression -- 14 Transform Methods -- 15 Conditional Expectation, Regression -- 16 Random Selection -- 17 Conditional Independence, Given a Random Vector -- 18 Appendices
520	0		_aThis is a "first course" in the sense that it presumes no previous course in probability. 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 we refer 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 several current 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, alternate implementations 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 of the structure of the relationships among events.
542	1		_fAttribution
546			_aIn English.
588	0		_aDescription based on print resource
650		0	_aMathematics _vTextbooks
650		0	_aApplied mathematics _vTextbooks
710	2		_aOpen Textbook Library _edistributor
856	4	0	_uhttps://open.umn.edu/opentextbooks/textbooks/256 _zAccess online version
999			_c38519 _d38519