Exact(1)
As illustrated in this figure, the proposed mixture approximation fits more precisely the empirical HTR obtained from the samples, while sample size is small and the statistics of distribution is impulsive.
Similar(59)
Moreover, from Fig. 7 a, b, it can be apperceived that the proposed mixture approximation precisely fits the empirical PDF and the tail of the empirical data with respect to those of the HTR-LM1, HTR-LM2, and HTR-FLOM mixture approximation models for ultrasound images taken from the pancreas and breast mass, respectively.
Two-component Gaussian mixture approximation - The density is approximated by fitting a two component Gaussian mixture a 1 N ( μ 1, σ 1 2 ) + a 2 N ( μ 2, σ 2 2 ), where μ 1, μ 2, σ 1 2, σ 2 2, a 1, and a2 are estimated from the observations.
The linear approximation fits for very small SINR ranges; the approximation fits better for medium SINR ranges.
In the following, the proposed mixture approximation based on α and γ with mixture ratio estimation is introduced.
We also compute the relative errors of the two approximations and find that the modified Gaussian mixture approximation is superior than the simple Gaussian mixture approximation nearly in the whole slow regime (Fig. 3 d, e, f).
This is the main reason that simple Gaussian mixture approximation has relatively large errors.
This simple Gaussian mixture approximation is shown and compared with the accurate distribution in Fig. 3 a, b.
We call Eq. 9 the modified Gaussian mixture approximation and compare it with the accurate distribution in Fig. 3 c.
Specifically, three major mixture approximations of HTR distribution are given.
Fig. 2 HTR distribution and its mixture approximations.
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