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  2. Numeric precision in Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Numeric_precision_in...

    Thus the 're-subtracting' of 1 leaves a mantissa ending in '100000000000000' instead of '010111000110010', representing a value of '1.1111111111117289E-4' rounded by Excel to 15 significant digits: '1.11111111111173E-4'. Of course mathematical 1 + x1 = x, 'floating point math' is sometimes a little different, that is not to be blamed on ...

  3. Single-index model - Wikipedia

    en.wikipedia.org/wiki/Single-index_model

    The single-index model (SIM) is a simple asset pricing model to measure both the risk and the return of a stock. The model has been developed by William Sharpe in 1963 and is commonly used in the finance industry.

  4. Hash function - Wikipedia

    en.wikipedia.org/wiki/Hash_function

    i = p i, so they are all 0 or 1. If R(x) = r n−1 x n−1 + ⋯ + r 1 x + r 0 is any nonzero polynomial modulo 2 with at most t nonzero coefficients, then R(x) is not a multiple of P(x) modulo 2. [Notes 4] If follows that the corresponding hash function will map keys with fewer than t bits in common to unique indices. [3]: 542–543

  5. Cross-validation (statistics) - Wikipedia

    en.wikipedia.org/wiki/Cross-validation_(statistics)

    In 2-fold cross-validation, we randomly shuffle the dataset into two sets d 0 and d 1, so that both sets are equal size (this is usually implemented by shuffling the data array and then splitting it in two). We then train on d 0 and validate on d 1, followed by training on d 1 and validating on d 0.

  6. Remainder - Wikipedia

    en.wikipedia.org/wiki/Remainder

    Its existence is based on the following theorem: Given two univariate polynomials a(x) and b(x) (where b(x) is a non-zero polynomial) defined over a field (in particular, the reals or complex numbers), there exist two polynomials q(x) (the quotient) and r(x) (the remainder) which satisfy: [7]

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  9. Information theory - Wikipedia

    en.wikipedia.org/wiki/Information_theory

    (Here, I(x) is the self-information, which is the entropy contribution of an individual message, and is the expected value.) A property of entropy is that it is maximized when all the messages in the message space are equiprobable p(x) = 1/n; i.e., most unpredictable, in which case H(X) = log n.