Probabilities, Random Variables, and Random Processes: Digital and Analog |
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Page 155
... variables ) is defined as Pxy ( a ;, ß ; ) = P [ ( X = a ; ) ^ ( Y = ß ; ) ] ( 5.3 ) or , in words , the joint mass function is 155 Chapter 5 Two Random Variables, Associated Functions and Usage Joint Distribution, Density, and Mass ...
... variables ) is defined as Pxy ( a ;, ß ; ) = P [ ( X = a ; ) ^ ( Y = ß ; ) ] ( 5.3 ) or , in words , the joint mass function is 155 Chapter 5 Two Random Variables, Associated Functions and Usage Joint Distribution, Density, and Mass ...
Page 156
... function for two random variables defined on S resolves itself to a problem in discrete probability theory . Second , it will be demonstrated that knowing the cumulative distribution or joint density or joint mass function of two random ...
... function for two random variables defined on S resolves itself to a problem in discrete probability theory . Second , it will be demonstrated that knowing the cumulative distribution or joint density or joint mass function of two random ...
Page 171
... joint cumulative distribution function is a problem in discrete probability ... mass functions , the case of discrete random variables will be discussed and ... joint mass function is defined as Pxy ( a , B ) = P [ ( X = a ) ^ ( Y = ẞ ) ...
... joint cumulative distribution function is a problem in discrete probability ... mass functions , the case of discrete random variables will be discussed and ... joint mass function is defined as Pxy ( a , B ) = P [ ( X = a ) ^ ( Y = ẞ ) ...
Contents
1 | 3 |
5 | 54 |
General Formulation and Solution of Problems | 69 |
Copyright | |
12 other sections not shown
Common terms and phrases
A₁ autocorrelation function average axiom B₁ balls Chapter convolution correlation integrals cross-correlation cross-correlation function cumulative distribution function dadß defined definition delta function denoted derivation deterministic discrete Fourier transform discrete probability theory discrete random process DRILL SET ensemble ergodic evaluate event space Example finite first-order stationary formula fundamental theorem fxy(a fy(B fz(y given impulse response joint density function joint mass function Laplace transform linear system M₁ member waveform notation obtained otherwise output periodic function periodic waveform permutations plotted in Figure points power spectral density probability measure probability theory problem properties pulse px(a random input random phenomenon range Rxx(T sample description space sampled values second-order stationary set theory shown in Figure shown plotted signal sketch SOLUTION solved statistics Sxx(w Ticket Pays two-sided Laplace transform two-sided z transform typical member X₁ z transform zero-mean