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  2. Law of large numbers - Wikipedia

    en.wikipedia.org/wiki/Law_of_large_numbers

    [16] [1] Stated for the case where X 1, X 2, ... is an infinite sequence of independent and identically distributed (i.i.d.) Lebesgue integrable random variables with expected value E(X 1) = E(X 2) = ... = μ, both versions of the law state that the sample average

  3. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is a part of the NumPy ecosystem array libraries [7] and is widely adopted to utilize GPU with Python, [8] especially in high-performance computing environments such as Summit, [9] Perlmutter, [10] EULER, [11] and ABCI.

  4. NumPy - Wikipedia

    en.wikipedia.org/wiki/NumPy

    [10] [11] Numarray had faster operations for large arrays, but was slower than Numeric on small ones, [12] so for a time both packages were used in parallel for different use cases. The last version of Numeric (v24.2) was released on 11 November 2005, while the last version of numarray (v1.5.2) was released on 24 August 2006. [13]

  5. Multinomial distribution - Wikipedia

    en.wikipedia.org/wiki/Multinomial_distribution

    The +2 in the name wald+2 can now be taken to mean that in the context of a two-by-two contingency table, which is a multinomial distribution with four possible events, then since we add 1/2 an observation to each of them, then this translates to an overall addition of 2 observations (due to the prior).

  6. Random number generation - Wikipedia

    en.wikipedia.org/wiki/Random_number_generation

    Dice are an example of a mechanical hardware random number generator. When a cubical die is rolled, a random number from 1 to 6 is obtained. Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols is generated that cannot be reasonably predicted better than by random chance.

  7. Rejection sampling - Wikipedia

    en.wikipedia.org/wiki/Rejection_sampling

    Rejection sampling is based on the observation that to sample a random variable in one dimension, one can perform a uniformly random sampling of the two-dimensional Cartesian graph, and keep the samples in the region under the graph of its density function. [1] [2] [3] Note that this property can be extended to N-dimension functions.

  8. 68–95–99.7 rule - Wikipedia

    en.wikipedia.org/wiki/68–95–99.7_rule

    In statistics, the 68–95–99.7 rule, also known as the empirical rule, and sometimes abbreviated 3sr or 3 σ, is a shorthand used to remember the percentage of values that lie within an interval estimate in a normal distribution: approximately 68%, 95%, and 99.7% of the values lie within one, two, and three standard deviations of the mean ...

  9. Percentage - Wikipedia

    en.wikipedia.org/wiki/Percentage

    To calculate a percentage of a percentage, convert both percentages to fractions of 100, or to decimals, and multiply them. For example, 50% of 40% is: ⁠ 50 / 100 ⁠ × ⁠ 40 / 100 ⁠ = 0.50 × 0.40 = 0.20 = ⁠ 20 / 100 ⁠ = 20%. It is not correct to divide by 100 and use the percent sign at the same time; it would literally imply ...