- Probabilistic analysis of algorithms
In

analysis of algorithms ,**probabilistic analysis of algorithms**is an approach to estimate thecomputational complexity of analgorithm or a computational problem. It starts from an assumption about a probabilistic distribution of the set of all possible inputs. This assumption is then used to design an efficient algorithm or to derive the complexity of a known algorithms.This approach is not the same as that of

probabilistic algorithm s, but the two may be combined.For non-probabilistic, more specifically, for

deterministic algorithm s, the most common types of complexity estimates are

*theaverage-case complexity (**expected time complexity**), in which given an input distribution, theexpected time of an algorithm is evaluated

*the**almost always**complexity estimates, in which given an input distribution, it is evaluated that the algorithm admits a given complexity estimate thatalmost surely holds.In probabilistic analysis of probabilistic (randomized) algorithms, the distributions or averaging for all possible choices in randomized steps are also taken into an account, in addition to the input distributions.

**ee also***

Amortized analysis

*Average-case complexity

*Best, worst and average case

*Random self-reducibility

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