Results 131 to 140 of about 676,552 (170)
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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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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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2019
The sequential use of random numbers, to sample the values of probability variables, allows obtaining solutions to mathematical problems such as the Monte Carlo method, that allows to model stochastic parameters or deterministic based on random sampling.
Lorenzo Cevallos-Torres +1 more
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The sequential use of random numbers, to sample the values of probability variables, allows obtaining solutions to mathematical problems such as the Monte Carlo method, that allows to model stochastic parameters or deterministic based on random sampling.
Lorenzo Cevallos-Torres +1 more
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Multicanonical Monte Carlo Simulations
International Journal of Modern Physics C, 1993The multicanonical ensemble is reviewed. Simulations of this ensemble promise improvements for a wide range of systems. Applications to first order phase transitions and spin glasses are summarized.
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Monte Carlo and Quasi-Monte Carlo Simulation
2014Is 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 +1 more
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2006
In the general overview on materials and their characteristics, outlined in Sect. 1.3, it has been stated that materials and their characteristics result from the processing of matter. Thus, condensed matter physics is one of the fundamentals for the understanding of materials.
Xiao Hu +2 more
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In the general overview on materials and their characteristics, outlined in Sect. 1.3, it has been stated that materials and their characteristics result from the processing of matter. Thus, condensed matter physics is one of the fundamentals for the understanding of materials.
Xiao Hu +2 more
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2013
The purpose of this chapter is to provide a practical guide to conduct Monte Carlo simulation as an important numerical technique to solve various problems encountered in hydrosystem engineering. As this method uses repeated random sampling to find approximate solutions by generating sequences of random numbers, the basic theory behind random numbers ...
Ehsan Goodarzi +2 more
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The purpose of this chapter is to provide a practical guide to conduct Monte Carlo simulation as an important numerical technique to solve various problems encountered in hydrosystem engineering. As this method uses repeated random sampling to find approximate solutions by generating sequences of random numbers, the basic theory behind random numbers ...
Ehsan Goodarzi +2 more
+4 more sources
Zeitschrift für Risikomanagement (ZfRM), 2023
Navid Azarafroz, Nils J. Balke
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Navid Azarafroz, Nils J. Balke
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2016
Das Kapitel 3 wendet sich der Monte-Carlo-Simulation von Optionen zu. Allgemeine stochastische Differentialgleichungen mussen hierzu numerisch integriert werden — das erste Thema des Kapitels. Der Prototyp einer solchen Methode ist das Euler-Verfahren, mit dem sich die meisten Beispiele simulieren lassen.
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Das Kapitel 3 wendet sich der Monte-Carlo-Simulation von Optionen zu. Allgemeine stochastische Differentialgleichungen mussen hierzu numerisch integriert werden — das erste Thema des Kapitels. Der Prototyp einer solchen Methode ist das Euler-Verfahren, mit dem sich die meisten Beispiele simulieren lassen.
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2020
In this chapter, we describe the Monte Carlo (MC) simulation used in this analysis. The purposes of the MC simulation are to estimate the signal acceptance for the \(K_L \!\rightarrow \! \pi ^0 \nu \overline{\nu }\) and \(K_L \!\rightarrow \! \pi ^0 X^0\) decays the acceptance for the normalization mode decays, namely \(K_L \!\rightarrow \! 3\
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In this chapter, we describe the Monte Carlo (MC) simulation used in this analysis. The purposes of the MC simulation are to estimate the signal acceptance for the \(K_L \!\rightarrow \! \pi ^0 \nu \overline{\nu }\) and \(K_L \!\rightarrow \! \pi ^0 X^0\) decays the acceptance for the normalization mode decays, namely \(K_L \!\rightarrow \! 3\
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