1. Define a domain of inputs
2. Generate inputs randomly
3. Perform a deterministic computation for each input
4.Aggregate the results of the individual computations into the final result
For example to estimate value of PI:
Example Monte-Carlo in games:
One of the main problems that this approach has in game playing is that it sometimes misses an isolated, very good move. These approaches are often strong strategically but weak tactically, as tactical decisions tend to rely on a small number of crucial moves which are easily missed by the randomly searching Monte Carlo algorithm.
In case of What-if
input variable are manually chosen (such as best case, worst case, and most likely case), and the results recorded for each so-called “what if” scenario.
a comparison of a spreadsheet cost construction model run using traditional “what if” scenarios, and then run again with Monte Carlo simulation and Triangular probability distributions shows that the Monte Carlo analysis has a narrower range than the “what if” analysis. This is because the “what if” analysis gives equal weight to all scenarios.
Monte Carlo integration
Just like the way it calculated the value of PI above, it puts a bounding box around the function and the ratio of the numbers in the box within the function vs the total points will be the estimate of the integration.
Optimization (e.g. minima of a function)
Bayes net inference???
SLAM (Simultaneous localization and mapping)
Radar return tracking