Probability and stochastic processes: a friendly introduction for electrical and computer engineersThis userfriendly resource will help you grasp the concepts of probability and stochastic processes, so you can apply them in professional engineering practice. The book presents concepts clearly as a sequence of building blocks that are identified either as an axiom, definition, or theorem. This approach provides a better understanding of the material, which can be used to solve practical problems. Key Features:

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i do not understand how a school can use this book as a so called teaching aid. There are very few examples and no answers to the homework problems. There is a quiz for each section that actually has answers on the publishers website. If you need something to level a table or kill a bug, this is the book for you. If you need a book with many examples and these new things called "answers", then look elsewhere. also, it is too bad i had to give this book one star instead of the zero it deserved.
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Excellent Book
Contents
Experiments Models and Probabilities  1 
Discrete Random Variables  49 
Continuous Random Variables  101 
Copyright  
11 other sections not shown
Other editions  View all
Probability and Stochastic Processes: A Friendly Introduction for Electrical ... Roy D. Yates,David Goodman No preview available  1998 
Probability and Stochastic Processes: A Friendly Introduction for Electrical ... Roy D. Yates,David Goodman No preview available  2014 
Probability and Stochastic Processes: A Friendly Introduction for Electrical ... Roy D. Yates,David J. Goodman No preview available  2005 
Common terms and phrases
arrival autocorrelation function average Bernoulli binomial calculate central limit theorem circuit coefficient communicating class conditional expected value conditional PDF conditional probability continuous random variables correlation corresponding Definition density function derive discrete random variables discretetime Equation event space Example experiment filter Find the PDF finite flip fx(x Fy(y Gaussian random variables given graph hypothesis test input integral interval joint PDF limiting state probabilities linear estimator marginal PDF Markov chain Matlab Matlab function mean square error minimum mean square minutes notation observe otherwise outcomes output packet parameter Poisson process power spectral density probability model probability vector Problem process X(t Proof properties Px(x queue Quiz random process random sequence random vector resistor sample function sample mean sample space sample value signal stationary probabilities stationary process stochastic process subexperiment Var[X variance wide sense stationary zero