Monte-Carlo Method
Monte-Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. They are often used when it is infeasible or impossible to compute an exact result with a deterministic algorithm.
Really, it’s just anything that uses randomness to solve problems that might be deterministic in principle. Common applications include:
- Numerical integration
- Optimization
- Simulating physical and mathematical systems
General Process
- Define a domain of possible inputs.
- Generate inputs randomly using a probability distribution over the domain.
- Perform a deterministic computation of the outputs.
- Aggregate the results.