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  2. Gaussian function - Wikipedia

    en.wikipedia.org/wiki/Gaussian_function

    The peak is "well-sampled", so that less than 10% of the area or volume under the peak (area if a 1D Gaussian, volume if a 2D Gaussian) lies outside the measurement region. The width of the peak is much larger than the distance between sample locations (i.e. the detector pixels must be at least 5 times smaller than the Gaussian FWHM).

  3. Spectral line shape - Wikipedia

    en.wikipedia.org/wiki/Spectral_line_shape

    A spectroscopic peak may be fitted to multiples of the above functions or to sums or products of functions with variable parameters. [6] The above functions are all symmetrical about the position of their maximum. [note 2] Asymmetric functions have also been used. [7] [note 3]

  4. Voigt profile - Wikipedia

    en.wikipedia.org/wiki/Voigt_profile

    The pseudo-Voigt profile (or pseudo-Voigt function) is an approximation of the Voigt profile V(x) using a linear combination of a Gaussian curve G(x) and a Lorentzian curve L(x) instead of their convolution. The pseudo-Voigt function is often used for calculations of experimental spectral line shapes.

  5. Full width at half maximum - Wikipedia

    en.wikipedia.org/wiki/Full_width_at_half_maximum

    In a distribution, full width at half maximum (FWHM) is the difference between the two values of the independent variable at which the dependent variable is equal to half of its maximum value. In other words, it is the width of a spectrum curve measured between those points on the y -axis which are half the maximum amplitude.

  6. Rayleigh distribution - Wikipedia

    en.wikipedia.org/wiki/Rayleigh_distribution

    Suppose is a random vector with components , that follows a multivariate t-distribution.If the components both have mean zero, equal variance and are independent, the bivariate Student's-t distribution takes the form:

  7. Multimodal distribution - Wikipedia

    en.wikipedia.org/wiki/Multimodal_distribution

    The figure shows the probability density function (p.d.f.), which is an equally-weighted average of the bell-shaped p.d.f.s of the two normal distributions. If the weights were not equal, the resulting distribution could still be bimodal but with peaks of different heights. Figure 2. A bimodal distribution. Figure 3.

  8. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.

  9. Gaussian beam - Wikipedia

    en.wikipedia.org/wiki/Gaussian_beam

    The Gaussian function has a 1/e 2 diameter (2w as used in the text) about 1.7 times the FWHM.. At a position z along the beam (measured from the focus), the spot size parameter w is given by a hyperbolic relation: [1] = + (), where [1] = is called the Rayleigh range as further discussed below, and is the refractive index of the medium.

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    matlab find peak function of variable size array