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Two examples of such algorithms are the Karger–Stein algorithm [1] and the Monte Carlo algorithm for minimum feedback arc set. [2] The name refers to the Monte Carlo casino in the Principality of Monaco, which is well-known around the world as an icon of gambling. The term "Monte Carlo" was first introduced in 1947 by Nicholas Metropolis. [3]
Sawilowsky [56] distinguishes between a simulation, a Monte Carlo method, and a Monte Carlo simulation: a simulation is a fictitious representation of reality, a Monte Carlo method is a technique that can be used to solve a mathematical or statistical problem, and a Monte Carlo simulation uses repeated sampling to obtain the statistical ...
Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. In statistical mechanics applications prior to the introduction of the Metropolis algorithm, the method consisted of generating a large number of random configurations of the system, computing the properties of interest (such as energy or density) for each configuration ...
Variants of the Monte Carlo method: Direct simulation Monte Carlo; Quasi-Monte Carlo method; Markov chain Monte Carlo. Metropolis–Hastings algorithm. Multiple-try Metropolis — modification which allows larger step sizes; Wang and Landau algorithm — extension of Metropolis Monte Carlo
The general motivation to use the Monte Carlo method in statistical physics is to evaluate a multivariable integral. The typical problem begins with a system for which the Hamiltonian is known, it is at a given temperature and it follows the Boltzmann statistics .
As it is commonly the case for Monte-Carlo methods, this algorithm performs particularly well when the dimension is higher than , and one only needs a small set of values. Indeed, the computational cost increases linearly with the dimension, whereas the cost of grid dependant methods increase exponentially with the dimension.
In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution.Given a probability distribution, one can construct a Markov chain whose elements' distribution approximates it – that is, the Markov chain's equilibrium distribution matches the target distribution.
The goal of a multilevel Monte Carlo method is to approximate the expected value [] of the random variable that is the output of a stochastic simulation.Suppose this random variable cannot be simulated exactly, but there is a sequence of approximations ,, …, with increasing accuracy, but also increasing cost, that converges to as .