Probability, Random Variables, and Random Signal PrinciplesThere are now 134 examples and nearly 900 homework problems; and other topics expanded or added include discussion of probability as a relative frequency, permutations, combinations, transformations of random variables, ergodicity of random processes, laws of large numbers, estimation, various inequalities, properties of impulses, and chapter-end summaries. This new material will prove most useful for students concerned with modern digital systems."--BOOK JACKET. |
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Page 181
... sample function of a continuous random process . In this example , the network is the outcome in the underlying random experiment of selecting a network . ( The presumption is that many networks are available from which to choose ; this ...
... sample function of a continuous random process . In this example , the network is the outcome in the underlying random experiment of selecting a network . ( The presumption is that many networks are available from which to choose ; this ...
Page 189
... sample functions of processes are presumed to exist for all time . Specific averages of interest are the mean value x = A [ x ( t ) ] of a sample function ( a lowercase letter is used to imply a sample function ) , and the time ...
... sample functions of processes are presumed to exist for all time . Specific averages of interest are the mean value x = A [ x ( t ) ] of a sample function ( a lowercase letter is used to imply a sample function ) , and the time ...
Page 461
... sample space , 108 Rational power spectrums , 366 Rayleigh , Lord ( John William Strutt ) , 59n Rayleigh density function , 59 , 442 maximum value of , 73 mean of , 99 , 442 interval , 181 , 237n natural , 296 of random processes , 295 ...
... sample space , 108 Rational power spectrums , 366 Rayleigh , Lord ( John William Strutt ) , 59n Rayleigh density function , 59 , 442 maximum value of , 73 mean of , 99 , 442 interval , 181 , 237n natural , 296 of random processes , 295 ...
Contents
Venn Diagram Equality and Difference Union | 7 |
Joint Probability Conditional Probability Total | 18 |
The Random Variable | 107 |
Copyright | |
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Other editions - View all
Probability, Random Variables, and Random Signal Principles Peyton Z. Peebles No preview available - 2001 |
Common terms and phrases
amplitude applied assume autocorrelation function available power gain average power band-limited bandpass bandwidth CHAPTER characteristic function cos(wpt covariance cross-correlation cross-correlation function cross-power defined denoted discrete random variables discrete-time DT system ergodic event example expected value Fourier transform frequency fx(x fy(y gaussian random variables given impulse response independent random variables integral joint density function jointly wide-sense stationary k₁ Linear Systems lowpass mean value Multiple Random Variables noise figure noise power noise temperature Peebles power density spectrum power spectrum Problem properties random process random process X(t Random Signal Principles random variables X1 real constants resistor Rxy(t Ryy(t sample function sample space sequence signal x(t spectral stationary process statistically independent statistically independent random Systems with Random t₁ transfer function uncorrelated variance voltage W₁ W₂ waveform white noise wide-sense stationary X₁ xx(w Y₁ Y₂ zero zero-mean