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  2. Approximation error - Wikipedia

    en.wikipedia.org/wiki/Approximation_error

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Pages for logged out editors learn more

  3. Minimum mean square error - Wikipedia

    en.wikipedia.org/wiki/Minimum_mean_square_error

    Standard method like Gauss elimination can be used to solve the matrix equation for .A more numerically stable method is provided by QR decomposition method. Since the matrix is a symmetric positive definite matrix, can be solved twice as fast with the Cholesky decomposition, while for large sparse systems conjugate gradient method is more effective.

  4. Sensitivity analysis - Wikipedia

    en.wikipedia.org/wiki/Sensitivity_analysis

    Figure 1. Schematic representation of uncertainty analysis and sensitivity analysis. In mathematical modeling, uncertainty arises from a variety of sources - errors in input data, parameter estimation and approximation procedure, underlying hypothesis, choice of model, alternative model structures and so on.

  5. Uncertainty quantification - Wikipedia

    en.wikipedia.org/wiki/Uncertainty_quantification

    Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.

  6. Fermi problem - Wikipedia

    en.wikipedia.org/wiki/Fermi_problem

    A Fermi problem (or Fermi question, Fermi quiz), also known as an order-of-magnitude problem, is an estimation problem in physics or engineering education, designed to teach dimensional analysis or approximation of extreme scientific calculations. Fermi problems are usually back-of-the-envelope calculations.

  7. Catastrophic cancellation - Wikipedia

    en.wikipedia.org/wiki/Catastrophic_cancellation

    In numerical analysis, catastrophic cancellation [1] [2] is the phenomenon that subtracting good approximations to two nearby numbers may yield a very bad approximation to the difference of the original numbers.