Abstract
Generators of sequences of pseudorandom numbers are introduced, after a general discussion about the transition of a numeric sequence from a regular to a chaotic and poorly predictable regime. The main methods to extract pseudorandom numbers distributed according to the desired density function are presented. Monte Carlo methods are introduced, in particular, the hit-or-miss Monte Carlo and importance sampling. The use of Monte Carlo method for numerical integration is presented. Markov Chain Monte Carlo are discussed.
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Lista, L. (2023). Random Numbers and Monte Carlo Methods. In: Statistical Methods for Data Analysis. Lecture Notes in Physics, vol 1010. Springer, Cham. https://doi.org/10.1007/978-3-031-19934-9_4
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DOI: https://doi.org/10.1007/978-3-031-19934-9_4
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