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. |
From inside the book
Results 1-3 of 31
Page 102
... function of Problem 3.2-27 to find ( a ) the mean value , ( b ) the second moment , and ( c ) the variance of X. * 3.2-30 . Show that the characteristic function of a random variable having the bi- nomial density of ( 2.5-1 ) is • x ( w ) ...
... function of Problem 3.2-27 to find ( a ) the mean value , ( b ) the second moment , and ( c ) the variance of X. * 3.2-30 . Show that the characteristic function of a random variable having the bi- nomial density of ( 2.5-1 ) is • x ( w ) ...
Page 103
Peyton Peebles. * 3.3-1 . Show that any characteristic function | x ( @ ) ] ≤ ( w ) satisfies x ( 0 ) = 1 * 3.3-2 . The characteristic function for a gaussian random variable X , having a mean value of 0 , is 103 CHAPTER 3 : Operations ...
Peyton Peebles. * 3.3-1 . Show that any characteristic function | x ( @ ) ] ≤ ( w ) satisfies x ( 0 ) = 1 * 3.3-2 . The characteristic function for a gaussian random variable X , having a mean value of 0 , is 103 CHAPTER 3 : Operations ...
Page 174
... characteristic function for X and Y defined in Problem 5.1-3 . * 5.2-2 . Show that the joint characteristic function of N independent random variables X , having characteristic functions x , ( w ) is N • X. , ......... ..Xxy ( W ] ...
... characteristic function for X and Y defined in Problem 5.1-3 . * 5.2-2 . Show that the joint characteristic function of N independent random variables X , having characteristic functions x , ( w ) is N • X. , ......... ..Xxy ( W ] ...
Contents
Venn Diagram Equality and Difference Union | 7 |
Joint Probability Conditional Probability Total | 18 |
The Random Variable | 107 |
Copyright | |
20 other sections not shown
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