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A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output (or both) are ...
The number of claims N is a random variable, which is said to have a "claim number distribution", and which can take values 0, 1, 2, .... etc..For the "Panjer recursion", the probability distribution of N has to be a member of the Panjer class, otherwise known as the (a,b,0) class of distributions.
In computing, a Monte Carlo algorithm is a randomized algorithm whose output may be incorrect with a certain (typically small) probability. Two examples of such algorithms are the Karger–Stein algorithm [ 1 ] and the Monte Carlo algorithm for minimum feedback arc set .
Then, there might be a tie. Following the weight update rule in weighted majority algorithm, the predictions made by the algorithm would be randomized. The algorithm calculates the probabilities of experts predicting positive or negatives, and then makes a random decision based on the computed fraction: [further explanation needed] predict
Pages in category "Randomized algorithms" The following 44 pages are in this category, out of 44 total. This list may not reflect recent changes. ...
Las Vegas algorithms were introduced by László Babai in 1979, in the context of the graph isomorphism problem, as a dual to Monte Carlo algorithms. [3] Babai [4] introduced the term "Las Vegas algorithm" alongside an example involving coin flips: the algorithm depends on a series of independent coin flips, and there is a small chance of failure (no result).
A randomized algorithm makes a set of random choices. These random choices may be intricately related making it difficult to analyze it. In many of these cases Principle of Deferred Decisions is used. The idea behind the principle is that the entire set of random choices are not made in advance, but rather fixed only as they are revealed to the ...
The basic RO algorithm can then be described as: Initialize x with a random position in the search-space. Until a termination criterion is met (e.g. number of iterations performed, or adequate fitness reached), repeat the following: Sample a new position y by adding a normally distributed random vector to the current position x