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 10
... sample space . An illustra- tion would be the experiment " obtain a number by spinning the pointer on a wheel of chance numbered from 0 to 12. " Here any number s from 0 to 12 can result and S = { 0 < s < 12 } . Such a sample space is ...
... sample space . An illustra- tion would be the experiment " obtain a number by spinning the pointer on a wheel of chance numbered from 0 to 12. " Here any number s from 0 to 12 can result and S = { 0 < s < 12 } . Such a sample space is ...
Page 24
... sample space , ( 2 ) the events defined on the sample space , and ( 3 ) the probabilities of the events . We specify these three quantities below , begin- ning with the sample space , for a combined experiment . Combined Sample Space ...
... sample space , ( 2 ) the events defined on the sample space , and ( 3 ) the probabilities of the events . We specify these three quantities below , begin- ning with the sample space , for a combined experiment . Combined Sample Space ...
Page 108
... sample space . It is in reality a vector space where the components of any vector are the values of the random variables X and Y. The new space is sometimes called the range sample space ( Davenport , 1970 ) or the two - dimen- sional ...
... sample space . It is in reality a vector space where the components of any vector are the values of the random variables X and Y. The new space is sometimes called the range sample space ( Davenport , 1970 ) or the two - dimen- sional ...
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