04152nam 22005415i 4500001001800000003000900018005001700027007001500044008004100059020003700100024002500137041000800162050001300170050001600183050001500199072001700214072001600231072002300247072002300270082001600293100002800309245010600337260003800443264003800481300003600519336002600555337002600581338003600607347002400643505080200667520170701469650001703176650002203193650003903215650002303254650001703277650004103294650004903335650002203384650004203406710003403448773002003482776003603502856003803538912001403576942000703590999001303597978-0-387-24158-6DE-He21320141014113428.0cr nn 008mamaa100301s2006 xxu| s |||| 0|eng d a97803872415869978-0-387-24158-67 a10.1007/b1046452doi aeng 4aTK5102.9 4aTA1637-1638 4aTK7882.S65 7aTTBM2bicssc 7aUYS2bicssc 7aTEC0080002bisacsh 7aCOM0730002bisacsh04a621.3822231 aKay, Steven M.eauthor.10aIntuitive Probability and Random Processes Using MATLABĒh[electronic resource] /cby Steven M. Kay. 1aBoston, MA :bSpringer US,c2006. 1aBoston, MA :bSpringer US,c2006. aXVIII, 833 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda0 aComputer Simulation -- Basic Probability -- Conditional Probability -- Discrete Random Variables -- Expected Values for Discrete Random Variables -- Multiple Discrete Random Variables -- Conditional Probability Mass Functions -- Discrete N-Dimensional Random Variables -- Continuous Random Variables -- Expected Values for Continuous Random Variables -- Multiple Continuous Random Variables -- Conditional Probability Density Functions -- Continuous N-Dimensional Random Variables -- Probability and Moment Approximations Using Limit Theorems -- Basic Random Processes -- Wide Sense Stationary Random Processes -- Linear Systems and Wide Sense Stationary Random Processes -- Multiple Wide Sense Stationary Random Processes -- Gaussian Random Processes -- Poisson Random Processes -- Markov Chains. aIntuitive Probability and Random Processes using MATLABĒ is an introduction to probability and random processes that merges theory with practice. Based on the author$1 (Bs belief that only "hands-on" experience with the material can promote intuitive understanding, the approach is to motivate the need for theory using MATLAB examples, followed by theory and analysis, and finally descriptions of "real-world" examples to acquaint the reader with a wide variety of applications. The latter is intended to answer the usual question "Why do we have to study this?" Other salient features are: *heavy reliance on computer simulation for illustration and student exercises *the incorporation of MATLAB programs and code segments *discussion of discrete random variables followed by continuous random variables to minimize confusion *summary sections at the beginning of each chapter *in-line equation explanations *warnings on common errors and pitfalls *over 750 problems designed to help the reader assimilate and extend the concepts Intuitive Probability and Random Processes using MATLABĒ is intended for undergraduate and first-year graduate students in engineering. The practicing engineer as well as others having the appropriate mathematical background will also benefit from this book. About the Author Steven M. Kay is a Professor of Electrical Engineering at the University of Rhode Island and a leading expert in signal processing. He has received the Education Award "for outstanding contributions in education and in writing scholarly books and texts..." from the IEEE Signal Processing society and has been listed as among the 250 most cited researchers in the world in engineering.s 0aEngineering. 0aFourier analysis. 0aDistribution (Probability theory). 0aTelecommunication.14aEngineering.24aSignal, Image and Speech Processing.24aProbability Theory and Stochastic Processes.24aFourier Analysis.24aCommunications Engineering, Networks.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z978038724157940uhttp://dx.doi.org/10.1007/b104645 aZDB-2-ENG cEB c531d531