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 187
... stationary process is also first - order stationary because the second - order density function determines the lower , first - order , density . Now the correlation E [ X , X2 ] = E [ X ( t1 ) X ( t2 ) ] of a random process will , in ...
... stationary process is also first - order stationary because the second - order density function determines the lower , first - order , density . Now the correlation E [ X , X2 ] = E [ X ( t1 ) X ( t2 ) ] of a random process will , in ...
Page 261
... stationary process with nonzero mean value X0 . Show that ∞ Sxx ( w ) = 2πX2 8 ( ∞ ) + [ ° Cxx ( t ) e for dt 81 where Cxx ( T ) is the autocovariance function of X ( t ) . 7.2-6 . For a random process X ( t ) , assume that Rxx ( t ) ...
... stationary process with nonzero mean value X0 . Show that ∞ Sxx ( w ) = 2πX2 8 ( ∞ ) + [ ° Cxx ( t ) e for dt 81 where Cxx ( T ) is the autocovariance function of X ( t ) . 7.2-6 . For a random process X ( t ) , assume that Rxx ( t ) ...
Page 453
... stationary process , 187 time , 189 , 337 time average of , 228 of wide - sense stationary process , 187-188 Autocovariance function , 191 of complex process , 207 defined , 198 , 199 , 207 Fourier transform of , 261 of wide - sense ...
... stationary process , 187 time , 189 , 337 time average of , 228 of wide - sense stationary process , 187-188 Autocovariance function , 191 of complex process , 207 defined , 198 , 199 , 207 Fourier transform of , 261 of wide - sense ...
Contents
Venn Diagram Equality and Difference Union | 7 |
Joint Probability Conditional Probability Total | 18 |
The Random Variable | 107 |
Copyright | |
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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