Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer EngineersThis user-friendly 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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Page 373
... stationary random sequences . Given a random process X ( t ) with expected value μx ( t ) and autocorrelation Rx ( t , t ) , we can make the noisy observation Y ( t ) = X ( t ) ... process is 10.9 STATIONARY PROCESSES 373 Stationary Processes.
... stationary random sequences . Given a random process X ( t ) with expected value μx ( t ) and autocorrelation Rx ( t , t ) , we can make the noisy observation Y ( t ) = X ( t ) ... process is 10.9 STATIONARY PROCESSES 373 Stationary Processes.
Page 374
... process is stationary . Usually a stochastic process is not stationary . However , proving or disproving stationarity can be tricky . Curious readers may wish to determine which of the processes in earlier examples are stationary ...
... process is stationary . Usually a stochastic process is not stationary . However , proving or disproving stationarity can be tricky . Curious readers may wish to determine which of the processes in earlier examples are stationary ...
Page 376
... stationary stochastic process listed in Theo- rem 10.11 . Let X1 , X2 , ... be an iid random sequence . Is X1 , X2 , . . . a stationary random sequence ? 10.10 Wide Sense Stationary Stochastic Processes There are many applications of ...
... stationary stochastic process listed in Theo- rem 10.11 . Let X1 , X2 , ... be an iid random sequence . Is X1 , X2 , . . . a stationary random sequence ? 10.10 Wide Sense Stationary Stochastic Processes There are many applications of ...
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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 binary calculate central limit theorem circuits coefficient components conditional expected value conditional PDF conditional probability continuous random variables continuous-time correlation corresponding Cov[X covariance matrix Definition density function derive discrete random variables discrete-time Equation Example experiment exponential filter Find the CDF flip following theorem fx,y Fx(x Gaussian random variables Gaussian random vector given hypothesis test integral interval joint PDF limiting state probabilities linear estimator marginal PDF Markov chain MATLAB mean square error minimum mean square Mn(X notation observe otherwise outcomes output parameter PDF fx Poisson process probability model Problem Proof properties Px,y queue Quiz random sequence random vector sample mean sample space sample value signal stationary process stochastic process subexperiment transition Var[X Var[Y variance wide sense stationary Y₁