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Kernel-independent component analysis

In statistics, kernel-independent component analysis (kernel ICA) is an efficient algorithm for independent component analysis which estimates source components by optimizing a generalized variance co

Expectation–maximization algorithm

In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where t

Banburismus

Banburismus was a cryptanalytic process developed by Alan Turing at Bletchley Park in Britain during the Second World War. It was used by Bletchley Park's Hut 8 to help break German Kriegsmarine (nava

Ziggurat algorithm

The ziggurat algorithm is an algorithm for pseudo-random number sampling. Belonging to the class of rejection sampling algorithms, it relies on an underlying source of uniformly-distributed random num

Lander–Green algorithm

The Lander–Green algorithm is an algorithm, due to Eric Lander and for computing the likelihood of observed genotype data given a pedigree. It is appropriate for relatively small pedigrees and a large

Elston–Stewart algorithm

The Elston–Stewart algorithm is an algorithm for computing the likelihood of observed data on a pedigree assuming a general model under which specific genetic segregation, linkage and association mode

Count-distinct problem

In computer science, the count-distinct problem(also known in applied mathematics as the cardinality estimation problem) is the problem of finding the number of distinct elements in a data stream with

Metropolis–Hastings algorithm

In statistics and statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution from whi

Yamartino method

The Yamartino method is an algorithm for calculating an approximation of the standard deviation of wind direction during a single pass through the incoming data.

Odds algorithm

The odds algorithm (or Bruss algorithm) is a mathematical method for computing optimal strategies for a class of problems that belong to the domain of optimal stopping problems. Their solution follows

Least mean squares filter

Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean square of the error signal

Buzen's algorithm

In queueing theory, a discipline within the mathematical theory of probability, Buzen's algorithm (or convolution algorithm) is an algorithm for calculating the normalization constant G(N) in the Gord

Repeated median regression

In robust statistics, repeated median regression, also known as the repeated median estimator, is a robust linear regression algorithm.The estimator has a breakdown point of 50%. Although it is equiva

Iterative proportional fitting

The iterative proportional fitting procedure (IPF or IPFP, also known as biproportional fitting or biproportion in statistics or economics (input-output analysis, etc.), RAS algorithm in economics, ra

Laplace's approximation

In mathematics, Laplace's approximation fits an un-normalised Gaussian approximation to a (twice differentiable) un-normalised target density. In Bayesian statistical inference this is useful to simul

Levenberg–Marquardt algorithm

In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems. These minimiz

False nearest neighbor algorithm

Within abstract algebra, the false nearest neighbor algorithm is an algorithm for estimating the embedding dimension. The concept was proposed by Kennel et al. (1992). The main idea is to examine how

Wang and Landau algorithm

The Wang and Landau algorithm, proposed by Fugao Wang and David P. Landau, is a Monte Carlo method designed to estimate the density of states of a system. The method performs a non-Markovian random wa

HyperLogLog

HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset. Calculating the exact cardinality of the unique elements of a multiset requires

Pseudo-marginal Metropolis–Hastings algorithm

In computational statistics, the pseudo-marginal Metropolis–Hastings algorithm is a Monte Carlo method to sample from a probability distribution. It is an instance of the popular Metropolis–Hastings a

VEGAS algorithm

The VEGAS algorithm, due to G. Peter Lepage, is a method for reducing error in Monte Carlo simulations by using a known or approximate probability distribution function to concentrate the search in th

Helmert–Wolf blocking

The Helmert–Wolf blocking (HWB) is a least squares solution method for the solution of a sparse block system of linear equations. It was first reported by F. R. Helmert for use in geodesy problems in

Chi-square automatic interaction detection

Chi-square automatic interaction detection (CHAID) is a decision tree technique based on adjusted significance testing (Bonferroni correction, Holm-Bonferroni testing). The technique was developed in

Farr's laws

Farr's law is a law formulated by Dr. William Farr when he made the observation that epidemic events rise and fall in a roughly symmetrical pattern. The time-evolution behavior could be captured by a

Algorithms for calculating variance

Algorithms for calculating variance play a major role in computational statistics. A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums

Random sample consensus

Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence

Gauss–Newton algorithm

The Gauss–Newton algorithm is used to solve non-linear least squares problems, which is equivalent to minimizing a sum of squared function values. It is an extension of Newton's method for finding a m

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