About: Probabilistic analysis of algorithms

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In analysis of algorithms, probabilistic analysis of algorithms is an approach to estimate the computational complexity of an algorithm 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 algorithm.This approach is not the same as that of probabilistic algorithms, but the two may be combined.

Attributes	Values
rdf:type	programming language
rdfs:label	Probabilistic analysis of algorithms (en)
rdfs:comment	In analysis of algorithms, probabilistic analysis of algorithms is an approach to estimate the computational complexity of an algorithm 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 algorithm.This approach is not the same as that of probabilistic algorithms, but the two may be combined. (en)
sameAs	Probabilistic analysis of algorithms Probabilistic analysis of algorithms
dbp:wikiPageUsesTemplate	dbt:Algorithm-stub dbt:Nosources
Subject	Analysis of algorithms Randomized algorithms
gold:hypernym	Approach
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Probabilistic_analysis_of_algorithms?oldid=1020332354&ns=0
Wikipage page ID	15383889 (xsd:integer)
page length (characters) of wiki page	1496 (xsd:nonNegativeInteger)
Wikipage revision ID	1020332354 (xsd:integer)
has abstract	In analysis of algorithms, probabilistic analysis of algorithms is an approach to estimate the computational complexity of an algorithm 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 algorithm.This approach is not the same as that of probabilistic algorithms, but the two may be combined. For non-probabilistic, more specifically deterministic, algorithms, the most common types of complexity estimates are the average-case complexity and the almost-always complexity. To obtain the average-case complexity, given an input distribution, the expected time of an algorithm is evaluated, whereas for the almost-always complexity estimate, it is evaluated that the algorithm admits a given complexity estimate that almost surely holds. In probabilistic analysis of probabilistic (randomized) algorithms, the distributions or average of all possible choices in randomized steps is also taken into account, in addition to the input distributions. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Probabilistic_analysis_of_algorithms
is Wikipage redirect of	Average-case analysis Average-case computational complexity Probabilistic analysis
is Link from a Wikipage to another Wikipage of	NESSUS Probabilistic Analysis Software Asymptotic computational complexity Average-case complexity Best, worst and average case Giuseppe Pompilj Go-Stop Decision analysis cycle Ecolego Expected value of including uncertainty Luc Devroye Average-case analysis Average-case computational complexity Murray Campbell

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