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  2. Moving least squares - Wikipedia

    en.wikipedia.org/wiki/Moving_least_squares

    Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.

  3. Maximum likelihood estimation - Wikipedia

    en.wikipedia.org/wiki/Maximum_likelihood_estimation

    [2] [3] [4] If the likelihood function is differentiable , the derivative test for finding maxima can be applied. In some cases, the first-order conditions of the likelihood function can be solved analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when the random errors are ...

  4. Maximum likelihood sequence estimation - Wikipedia

    en.wikipedia.org/wiki/Maximum_likelihood...

    Suppose that there is an underlying signal {x(t)}, of which an observed signal {r(t)} is available.The observed signal r is related to x via a transformation that may be nonlinear and may involve attenuation, and would usually involve the incorporation of random noise.

  5. Least mean squares filter - Wikipedia

    en.wikipedia.org/wiki/Least_mean_squares_filter

    The Normalised least mean squares filter (NLMS) is a variant of the LMS algorithm that solves this problem by normalising with the power of the input. The NLMS algorithm can be summarised as: The NLMS algorithm can be summarised as:

  6. CLs method (particle physics) - Wikipedia

    en.wikipedia.org/wiki/CLs_method_(particle_physics)

    [4] Upper limits derived with the CLs method always contain the zero value of the parameter and hence the coverage probability at this point is always 100%. The definition of CLs does not follow from any precise theoretical framework of statistical inference and is therefore described sometimes as ad hoc.

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  8. Google JAX - Wikipedia

    en.wikipedia.org/wiki/Google_JAX

    JAX is a machine learning framework for transforming numerical functions. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).

  9. Maximum length sequence - Wikipedia

    en.wikipedia.org/wiki/Maximum_length_sequence

    A maximum length sequence (MLS) is a type of pseudorandom binary sequence.. They are bit sequences generated using maximal linear-feedback shift registers and are so called because they are periodic and reproduce every binary sequence (except the zero vector) that can be represented by the shift registers (i.e., for length-m registers they produce a sequence of length 2 m − 1).

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    difference between mls and cls in python 4 2 3 kjv mean in english language