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Random Numbers and Monte Carlo Methods

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Statistical Methods for Data Analysis in Particle Physics

Part of the book series: Lecture Notes in Physics ((LNP,volume 941))

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Abstract

Many computer application, ranging from simulations to video games and 3D-graphics, take advantage of computer-generated numeric sequences that have properties very similar to truly random variables. Sequences generated by computer algorithms through mathematical operations are not really random, having no intrinsic unpredictability, and are necessarily deterministic and reproducible. Indeed, the possibility to reproduce exactly the same sequence of computer-generated numbers with a computer algorithm is often a good feature for many application.

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Notes

  1. 1.

    In realistic cases of finite numeric precision, one of the extreme values is excluded. Each individual value would have a corresponding zero probability, in the case of infinite precision, but this is not exactly true with finite machine precision.

  2. 2.

    I.e. ∫ a b f(x) dx may be different from one.

  3. 3.

    See Sect. 5.9; a more rigorous approach is presented in Sect. 7.3.

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Lista, L. (2017). Random Numbers and Monte Carlo Methods. In: Statistical Methods for Data Analysis in Particle Physics. Lecture Notes in Physics, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-319-62840-0_4

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