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  2. Extrapolation - Wikipedia

    en.wikipedia.org/wiki/Extrapolation

    A sound choice of which extrapolation method to apply relies on a priori knowledge of the process that created the existing data points. Some experts have proposed the use of causal forces in the evaluation of extrapolation methods. [2] Crucial questions are, for example, if the data can be assumed to be continuous, smooth, possibly periodic, etc.

  3. Richardson extrapolation - Wikipedia

    en.wikipedia.org/wiki/Richardson_extrapolation

    In numerical analysis, Richardson extrapolation is a sequence acceleration method used to improve the rate of convergence of a sequence of estimates of some value = (). In essence, given the value of A ( h ) {\displaystyle A(h)} for several values of h {\displaystyle h} , we can estimate A ∗ {\displaystyle A^{\ast }} by extrapolating the ...

  4. Romberg's method - Wikipedia

    en.wikipedia.org/wiki/Romberg's_method

    The zeroeth extrapolation, R(n, 0), is equivalent to the trapezoidal rule with 2 n + 1 points; the first extrapolation, R(n, 1), is equivalent to Simpson's rule with 2 n + 1 points. The second extrapolation, R(n, 2), is equivalent to Boole's rule with 2 n + 1 points. The further extrapolations differ from Newton-Cotes formulas.

  5. Aitken's delta-squared process - Wikipedia

    en.wikipedia.org/wiki/Aitken's_delta-squared_process

    In numerical analysis, Aitken's delta-squared process or Aitken extrapolation is a series acceleration method used for accelerating the rate of convergence of a sequence. It is named after Alexander Aitken, who introduced this method in 1926. [1] It is most useful for accelerating the convergence of a sequence that is converging linearly.

  6. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Fitting of a noisy curve by an asymmetrical peak model, with an iterative process (Gauss–Newton algorithm with variable damping factor α).Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints.

  7. In vitro to in vivo extrapolation - Wikipedia

    en.wikipedia.org/wiki/In_vitro_to_in_vivo...

    To solve that extrapolation problem, more statistical models with mechanistic information are needed, or we can rely on mechanistic systems of biology models of the cell response. Those models are characterized by a hierarchical structure, such as molecular pathways, organ function, whole-cell response, cell-to- cell communications, tissue ...

  8. Today's Wordle Hint, Answer for #1257 on Wednesday, November ...

    www.aol.com/todays-wordle-hint-answer-1257...

    If you’re stuck on today’s Wordle answer, we’re here to help—but beware of spoilers for Wordle 1257 ahead. Let's start with a few hints.

  9. Nearest-neighbor interpolation - Wikipedia

    en.wikipedia.org/wiki/Nearest-neighbor_interpolation

    Nearest neighbor interpolation (blue lines) in one dimension on a (uniform) dataset (red points) Nearest neighbor interpolation on a uniform 2D grid (black points). Each colored cell indicates the area in which all the points have the black point in the cell as their nearest black point.