with high probability
{{Use American English|date=January 2019}}{{Short description|Description of limiting behavior in probabilistic algorithms}}
{{no footnotes|date=December 2023}}
In mathematics, an event that occurs with high probability (often shortened to w.h.p. or WHP) is one whose probability depends on a certain number n and goes to 1 as n goes to infinity, i.e. the probability of the event occurring can be made as close to 1 as desired by making n big enough.
Applications
The term WHP is especially used in computer science, in the analysis of probabilistic algorithms. For example, consider a certain probabilistic algorithm on a graph with n nodes. If the probability that the algorithm returns the correct answer is , then when the number of nodes is very large, the algorithm is correct with a probability that is very near 1. This fact is expressed shortly by saying that the algorithm is correct WHP.
Some examples where this term is used are:
- Miller–Rabin primality test: a probabilistic algorithm for testing whether a given number n is prime or composite. If n is composite, the test will detect n as composite WHP. There is a small chance that we are unlucky and the test will think that n is prime. But, the probability of error can be reduced indefinitely by running the test many times with different randomizations.
- Freivalds' algorithm: a randomized algorithm for verifying matrix multiplication. It runs faster than deterministic algorithms WHP.
- Treap: a randomized binary search tree. Its height is logarithmic WHP. Fusion tree is a related data structure.
- Online codes: randomized codes which allow the user to recover the original message WHP.
- BQP: a complexity class of problems for which there are polynomial-time quantum algorithms which are correct WHP.
- Probably approximately correct learning: A process for machine-learning in which the learned function has low generalization-error WHP.
- Gossip protocols: a communication protocol used in distributed systems to reliably deliver messages to the whole cluster using a constant amount of network resources on each node and ensuring no single point of failure.
See also
References
- {{Cite journal | doi = 10.1007/s00446-010-0121-5| title = An optimal bit complexity randomized distributed MIS algorithm| journal = Distributed Computing| volume = 23| issue = 5–6| pages = 331| year = 2010| last1 = Métivier | first1 = Y.| last2 = Robson | first2 = J. M.| last3 = Saheb-Djahromi | first3 = N.| last4 = Zemmari | first4 = A.}}
- {{cite web | url=http://dcg.ethz.ch/lectures/podc_allstars/lecture/chapter7.pdf | title=Principles of Distributed Computing (lecture 7) | publisher=ETH Zurich | accessdate=21 February 2015}}
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