Results 291 to 300 of about 851,166 (333)
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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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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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Monte Carlo and Quasi-Monte Carlo Methods
2013Chapter 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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Monte Carlo and quasi-Monte Carlo methods
Acta Numerica, 1998Monte 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 and Quasi-Monte Carlo Methods 2006
2008This book represents the refereed proceedings of the Seventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, held in Ulm (Germany) in August 2006. The proceedings include carefully selected papers on many aspects of Monte Carlo and quasi-Monte Carlo methods and their applications, as well as providing ...
Keller, A. +2 more
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2000
Introduction to multicanonical Monte Carlo simulations by B. A. Berg MCMC in $I \times J \times K$ contingency tables by F. Bunea and J. Besag Extension of Fill's perfect rejection sampling algorithm to general chains (Extended abstract) by J. A. Fill, M. Machida, D. J. Murdoch, and J. S.
Helmut Neunzert, Abul Hasan Siddiqi
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Introduction to multicanonical Monte Carlo simulations by B. A. Berg MCMC in $I \times J \times K$ contingency tables by F. Bunea and J. Besag Extension of Fill's perfect rejection sampling algorithm to general chains (Extended abstract) by J. A. Fill, M. Machida, D. J. Murdoch, and J. S.
Helmut Neunzert, Abul Hasan Siddiqi
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1998
The most common applications of Monte Carlo methods in numerical computations are for evaluating integrals. Monte Carlo methods can also be used in solving systems of equations (see Chapter 7 of Hammersley and Handscomb, 1964, for example), but other methods are generally better, especially for matrices that are not sparse.
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The most common applications of Monte Carlo methods in numerical computations are for evaluating integrals. Monte Carlo methods can also be used in solving systems of equations (see Chapter 7 of Hammersley and Handscomb, 1964, for example), but other methods are generally better, especially for matrices that are not sparse.
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2016
No way to compute the path integral of lattice QCD analytically is known. Even on a finite lattice it amounts of solving the very-high-dimensional integral of Eq. ( 1.85).
Michael Günther +2 more
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No way to compute the path integral of lattice QCD analytically is known. Even on a finite lattice it amounts of solving the very-high-dimensional integral of Eq. ( 1.85).
Michael Günther +2 more
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Rendiconti del Seminario Matematico e Fisico di Milano, 1973
Monte Carlo methods are introduced and their historical development is briefly outlined. Those methods are shown to the attractive techniques for some topics of modern applied mathematics. The usefulness of these randomized strategies is stressed for linear multivariable interpolation problems.
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Monte Carlo methods are introduced and their historical development is briefly outlined. Those methods are shown to the attractive techniques for some topics of modern applied mathematics. The usefulness of these randomized strategies is stressed for linear multivariable interpolation problems.
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1990
The Monte Carlo method may briefly be described as the numerical tool of studying an artificial stochastic model of a physical or mathematical process. This paper examines the basic principles of the method, which include the random numbers generation from an uniform density function, its extension for sampling from more complicated probability density
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The Monte Carlo method may briefly be described as the numerical tool of studying an artificial stochastic model of a physical or mathematical process. This paper examines the basic principles of the method, which include the random numbers generation from an uniform density function, its extension for sampling from more complicated probability density
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