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The Monte Carlo method

Journal of the American Statistical Association, 1949
Abstract In this paper Metropolis and Ulam gave a brief introduction to “the Monte Carlo method” which is described as a statistical approach to the study of differential equations as applied by Metropolis, Ulam, Fermi, von Neumann, Feynman, and others at the Los Alamos Laboratory in the 1940s.0 Several examples of applications of ...
N, METROPOLIS, S, ULAM
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Monte Carlo and Quasi-Monte Carlo Methods

2020
Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N~1^2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction ...
Tuffin, Bruno, L'Écuyer, Pierre
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The multicanonical Monte Carlo method

Computing in Science & Engineering, 2000
In recent years, several new Monte Carlo methods have proven to be very effective for sampling from multimodal energy landscapes, like those found near a first-order phase transition or in a glassy material. In this column, we will summarize the theoretical structure of one of these methods, the multicanonical method,1,2 as it is perhaps the most ...
James E. Gubernatis, Naomichi Hatano
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The Monte Carlo Method

Journal of the Society for Industrial and Applied Mathematics, 1958
A description of the many facets of the Monte Carlo Method is presented. The subject is traversed from the most elementary to the more difficult techniques, and from the least practical to the most fruitful applications. The generation of random numbers in the modern electronic computing machine is dealt with.
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Monte Carlo and Quasi-Monte Carlo Methods

2013
Chapter 12 discusses Monte Carlo and quasi-Monte Carlo methods and demonstrates how these techniques can be used to compute functionals of multidimensional diffusions. Monte Carlo methods feature prominently in this book, in particular we discuss how to use Lie Symmetry methods to construct unbiased Monte Carlo estimators in Chap. 6, and we discuss how
Jan Baldeaux, Eckhard Platen
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The other Monte Carlo method

Computing in Science & Engineering, 2006
Although the Metropolis algorithm dates back to at least 1953, the fact that it could be used for approximate counting has become clear only in recent years. An algorithm specifically designed for counting was created around the same time as the Metropolis algorithm by some of the same researchers. This other Monte Carlo method, now known as sequential
Isabel Beichl, Francis Sullivan
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Monte Carlo and quasi-Monte Carlo methods

Acta Numerica, 1998
Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction ...
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Monte Carlo methods in ICF

The 13th international conference on laser interactions and related plasma phenomena, 1997
Monte Carlo methods appropriate to simulate the transport of x-rays, neutrons, ions and electrons in Inertial Confinement Fusion targets are described and analyzed. The Implicit Monte Carlo method of x-ray transport handles symmetry within indirect drive ICF hohlraums well, but can be improved 50X in efficiency by angular biasing the x-rays towards the
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Monte Carlo Methods

1987
The term ‘Monte Carlo methods’ is used to refer to two different, though closely related, techniques. The first meaning, currently the less common one among economists, is the evaluation of definite integrals by use of random variables. The idea is to evaluate \(\int_a^b {F\left( x \right)} {\text{d}}x\) where x may be a vector) by estimating \(\int_a ...
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A monte carlo method for factorization

BIT, 1975
The following simple method will find small prime factors \(p\) of a number \(n\) in \(O(p^{1/2})\) arithmetical operations as opposed to the \(O(p)\) operations required by trial division. Let \(x_0=2\), \(x_{i+1}\equiv x_i^2-1\pmod n\) (other similar sequences may be used). Generate in turn the pairs \((x_j,x_{2j})\), accumulating the product \(\pmod
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