Stochastic Simulation: Random Number Generators

This lecture covers the definition of a random number generator (RNG) as a procedure producing an infinite stream of independent and identically distributed random variables. It explains the structure and algorithms of Pseudo-Random Number Generators (Pseudo-RNG), emphasizing the importance of properties like large period, statistical tests, efficiency, and reproducibility. Various types of generators, including Linear Congruential Generators (LCG) and Multiple Recursive Generators (MRG), are discussed. The assessment of RNG quality through statistical tests and the evaluation of independence are also explored. The lecture concludes with methods for testing independence in generated sequences.