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Median trick, the Glossary

Index Median trick

The median trick is a generic approach that increases the chances of a probabilistic algorithm to succeed.[1]

Table of Contents

  1. 8 relations: Approximate counting algorithm, Chernoff bound, Hash function, Median, Number, Randomized algorithm, Regression analysis, Time complexity.

  2. Statistical randomness

Approximate counting algorithm

The approximate counting algorithm allows the counting of a large number of events using a small amount of memory.

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Chernoff bound

In probability theory, a Chernoff bound is an exponentially decreasing upper bound on the tail of a random variable based on its moment generating function.

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Hash function

A hash function is any function that can be used to map data of arbitrary size to fixed-size values, though there are some hash functions that support variable length output.

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The median of a set of numbers is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution.

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Number

A number is a mathematical object used to count, measure, and label.

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Randomized algorithm

A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure.

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Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features').

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Time complexity

In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm.

See Median trick and Time complexity

See also

Statistical randomness

References

[1] https://en.wikipedia.org/wiki/Median_trick