Editing Randomization (section)

=== Simulation ===
{{Main|Simulation}}
In many scientific and engineering fields, computer simulations of real phenomena are commonly used. When the real phenomena are affected by unpredictable processes, such as radio noise or day-to-day weather, these processes can be simulated using random or pseudo-random numbers. One of the most prominent uses of randomization in simulations is in [[Monte Carlo method|Monte Carlo methods]]. These methods rely on repeated random sampling to obtain numerical results, typically to model probability distributions or to estimate uncertain quantities in a system.

Randomization also allows for the testing of models or algorithms against unexpected inputs or scenarios. This is essential in fields like machine learning and artificial intelligence, where algorithms must be robust against a variety of inputs and conditions.<ref>{{Cite thesis |title=On the impact of randomization on robustness in machine learning |url=https://hal.science/tel-03121555 |publisher=Université Paris sciences et lettres |date=2020-12-02 |degree=phdthesis |language=en |first=Rafael |last=Pinot}}</ref>