Monte Carlo techniques use some form of random choices as part of their algorithms. Examples include random forests and Monte Carlo search. These can be practical algorithms, especially where the choice is not entirely random, but guided by heuristics to enable different chocies to have different probabilities. Monto Carlo algroithms can also be used as a base case to compare with other algorithms. For example, a simple Monte Carlo optimisation algorithm is to simply choose parameters at random and keep track of the best seen so far; if other algorithms do not perform significantly better than this on a data set, then they are clearly not well suited to the kind of data.
Defined on page 142
Used on page 142
Also known as monte carlo algorithm