Results 281 to 290 of about 851,166 (333)
A new method to assess the statistical convergence of monte carlo solutions
, 1991 R.A. Forster
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Criticality accident detector coverage analysis using the Monte Carlo Method
, 1993 J.F. Zino, Kennedy Chinedu Okafor
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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 Monte ...
Stanislaw M. Ulam, N. Metropolis
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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 Monte ...
Stanislaw M. Ulam, N. Metropolis
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2015
Among other programming techniques for equity markets, Monte Carlo simulation has a special place due to its wide applicability and relatively easy implementation compared to exact, non-stochasatic methods. These algorithms can be used in many applications such as price forecasting and the validation of certain buying strategies, for example.
Kjell A. Doksum, Peter J. Bickel
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Among other programming techniques for equity markets, Monte Carlo simulation has a special place due to its wide applicability and relatively easy implementation compared to exact, non-stochasatic methods. These algorithms can be used in many applications such as price forecasting and the validation of certain buying strategies, for example.
Kjell A. Doksum, Peter J. Bickel
+6 more sources
GEM - International Journal on Geomathematics, 2017
Monte Carlo methods deal with generating random variates from probability density functions in order to estimate unknown parameters or general functions of unknown parameters and to compute their expected values, variances and covariances. One generally works with the multivariate normal distribution due to the central limit theorem. However, if random
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Monte Carlo methods deal with generating random variates from probability density functions in order to estimate unknown parameters or general functions of unknown parameters and to compute their expected values, variances and covariances. One generally works with the multivariate normal distribution due to the central limit theorem. However, if random
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2018
Stochastic-simulation, or Monte-Carlo, methods are used extensively in the area of credit-risk modelling. This technique has, in fact, been employed inveterately in previous chapters. Care and caution are always advisable when employing a complex numerical technique. Prudence is particularly appropriate, in this context, because default is a rare event.
Kavita Bala +2 more
+7 more sources
Stochastic-simulation, or Monte-Carlo, methods are used extensively in the area of credit-risk modelling. This technique has, in fact, been employed inveterately in previous chapters. Care and caution are always advisable when employing a complex numerical technique. Prudence is particularly appropriate, in this context, because default is a rare event.
Kavita Bala +2 more
+7 more sources
Monte Carlo and Quasi-Monte Carlo Methods
2020Monte 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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1999
Nature is composed of gross assemblies of huge numbers of atoms and molecules showing a wide variety of phenomena according to the way how they are assembling. The macroscopic behaviors of such systems are rather different from the microscopic laws in the world of atoms and molecules.
Yoshiyuki Kawazoe +2 more
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Nature is composed of gross assemblies of huge numbers of atoms and molecules showing a wide variety of phenomena according to the way how they are assembling. The macroscopic behaviors of such systems are rather different from the microscopic laws in the world of atoms and molecules.
Yoshiyuki Kawazoe +2 more
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2011
A general problem in probability and statistical applications is the computation of an expectation of a random variable. We illustrate the use of R to compute some expectations by the Monte Carlo method. These computations are helpful in comparing sampling properties of point estimators or evaluating probabilities of coverage of interval estimators. We
Maria L. Rizzo, Jim Albert
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A general problem in probability and statistical applications is the computation of an expectation of a random variable. We illustrate the use of R to compute some expectations by the Monte Carlo method. These computations are helpful in comparing sampling properties of point estimators or evaluating probabilities of coverage of interval estimators. We
Maria L. Rizzo, Jim Albert
openaire +2 more sources