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 x
... Properties of Conditional Density Methods of Defining Conditioning Event 2.7 Summary Problems - 3 Operations on One ... Properties 109 Joint Distribution Function | Properties of the Joint Distribution Marginal Distribution Functions 4.3 ...
... Properties of Conditional Density Methods of Defining Conditioning Event 2.7 Summary Problems - 3 Operations on One ... Properties 109 Joint Distribution Function | Properties of the Joint Distribution Marginal Distribution Functions 4.3 ...
Page 49
... properties are left to the reader as exercises . Properties 1 and 2 require that the density function be nonnegative and have an area of unity . These two properties may also be used as tests to see if some function , say , gx ( x ) ...
... properties are left to the reader as exercises . Properties 1 and 2 require that the density function be nonnegative and have an area of unity . These two properties may also be used as tests to see if some function , say , gx ( x ) ...
Page 111
... properties that follow readily from its definition . We list them : ( 1 ) Fx.x ( -∞ , ∞ ) = 0 Fx.y ( ∞ , y ) = 0 ... properties are just the two - dimensional extensions of the properties of one random variable given in ( 2.2-2 ) ...
... properties that follow readily from its definition . We list them : ( 1 ) Fx.x ( -∞ , ∞ ) = 0 Fx.y ( ∞ , y ) = 0 ... properties are just the two - dimensional extensions of the properties of one random variable given in ( 2.2-2 ) ...
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 |
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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