[2] The name refers to the Monte Carlo casino in the Principality of Monaco, which is well-known around the world as an icon of gambling.
Whether this process is a Las Vegas algorithm depends on whether halting with probability one is considered to satisfy the definition.
The answer they provide (either true or false) will be incorrect, or correct, with some bounded probability.
The complexity class BPP describes decision problems that can be solved by polynomial-time Monte Carlo algorithms with a bounded probability of two-sided errors, and the complexity class RP describes problems that can be solved by a Monte Carlo algorithm with a bounded probability of one-sided error: if the correct answer is false, the algorithm always says so, but it may answer false incorrectly for some instances where the correct answer is true.
[4] In contrast, the complexity class ZPP describes problems solvable by polynomial expected time Las Vegas algorithms.
[4] Another complexity class, PP, describes decision problems with a polynomial-time Monte Carlo algorithm that is more accurate than flipping a coin but where the error probability cannot necessarily be bounded away from 1⁄2.
[4] Randomized algorithms are primarily divided by its two main types, Monte Carlo and Las Vegas, however, these represent only a top of the hierarchy and can be further categorized.
"[4] (stronger bound than regular LV) Sherwood probabilistic will inhibit usefulness of the algorithm; typical case is
) Previous table represents a general framework for Monte Carlo and Las Vegas randomized algorithms.