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ensemble size, filter bandwidth, or bit resolution of the digitizer card. 

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So far, we have studied time-independent ensemble variances because 〈 f ( t ) $^{2}$〉−〈 f ( t ) 〉 $^{2}$= 0 always holds for deterministic signals. Time-dependent variance signals require statistical mixtures of signals. As a first example, a sequence of coherent microwave pulses with alternating phase shifts ± ϕ is applied to the input line. This corresponds to a statistical mixture with an equally distributed histogram. The mean value and cross-variance are A cos( ϕ ) sin( ωt ) and − α cos $^{2}$( ωt ), respectively, where α = A $^{2}$sin$^{2}$( ϕ ) is the cross-variance amplitude and ω/ 2 π = 10 MHz is the signal frequency after the mixers. The frequency doubling cos $^{2}$( ωt ) ∝ [1+ cos(2 ωt )] is confirmed by the data shown in Fig. 3a. Repeating the experiment for different phase shifts, we reproduce the expected sin $^{2}$( ϕ )-dependence of α for 10 and 100 poa (cf. Fig. 3b). 

Studying signal mixtures also allows us to address time-dependent third central moments 〈 ( S −〈 S 〉 ) $^{3}$〉 and 〈 ( χ$_{1}$ −〈 χ$_{1}$ 〉 ) $^{3}$〉 . They are proportional to the skewness of their associated probability histograms and vanish for Gaussian distributions. In particular, both quantities 

