Results 251 to 260 of about 1,160,391 (297)

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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Monte Carlo radiosity

Computing, 1995
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Monte Carlo, Quasi-Monte Carlo, and Randomized Quasi-Monte Carlo

2000
This paper surveys recent research on using Monte Carlo techniques to improve quasi-Monte Carlo techniques. Randomized quasi-Monte Carlo methods provide a basis for error estimation. They have, in the special case of scrambled nets, also been observed to improve accuracy.
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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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Monte Carlo and Quasi-Monte Carlo Simulation

2014
Is this chapter we will learn the basics of pricing derivatives using simulation methods. We will consider both Monte-Carlo and quasi-Monte Carlo but – of course – with a special emphasis on the latter. The aim of our exposition is not to provide a large toolbox for the quantitative analyst, but to help getting started with the topic. QMC-pricing is an
Gunther Leobacher, Friedrich Pillichshammer   +1 more
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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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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 Simulations

2008
A description of Monte Carlo methods for simulation of proteins is given. Advantages and disadvantages of the Monte Carlo approach are presented. The theoretical basis for calculating equilibrium properties of biological molecules by the Monte Carlo method is presented.
David J, Earl, Michael W, Deem
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Filtered Monte Carlo

Mathematics of Operations Research, 1993
By a filtered Monte Carlo estimator we mean one whose constituent parts—summands or integral increments—are conditioned on an increasing family of σ-fields. Unbiased estimators of this type are suggested by compensator identities. Replacing a point-process integrator with its intensity gives rise to one class of examples; exploiting Levy's formula ...
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Learning Monte Carlo

Computing in Science & Engineering, 2017
Francis Sullivan reviews "Quantum Monte Carlo Methods", by James Gubernatis, Naoki Kawashima and Philipp Werner, declaring it a valuable and well-constructed manual on quantum Monte Carlo.
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