Results 211 to 220 of about 47,776 (252)
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Random Numbers and Monte Carlo Methods
Graduate Texts in Physics, 2013Philipp O J Scherer, Scherer Philipp O J
exaly +2 more sources
Quasi-Monte Carlo Methods in Numerical Finance
, 1996 This paper introduces and illustrates a new version of the Monte Carlo method that has attractive properties for the numerical valuation of derivatives.
C. Joy, P. Boyle, K. S. Tan
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Parallel Monte Carlo Methods for Derivative Security Pricing
Numerical Analysis and Its Applications, 2000 . Monte Carlo (MC) methods have proved to be flexible, robust and very useful techniques in computational finance. Several studies have investigated ways to achieve greater efficiency of such methods for serial computers. In this paper, we concentrate on
G. Pauletto
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jnanabha, 2020
Monte Carlo method is a powerful method for computing the value of complex integrals using probabilistic techniques and estimates the integrals or other quantities that can be expressed as an expectation by averaging the results of a high number of statistical trials.Its convergence rateO(√N), is independent of dimension and hence it is preferred for a
Saurabh Saxena +2 more
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Monte Carlo method is a powerful method for computing the value of complex integrals using probabilistic techniques and estimates the integrals or other quantities that can be expressed as an expectation by averaging the results of a high number of statistical trials.Its convergence rateO(√N), is independent of dimension and hence it is preferred for a
Saurabh Saxena +2 more
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Multilevel Monte Carlo by using the Halton sequence
Monte Carlo Methods Appl., 2020Monte Carlo (MC) simulation depends on pseudo-random numbers. The generation of these numbers is examined in connection with the Brownian motion. We present the low discrepancy sequence known as Halton sequence that generates different stochastic samples
Shady A. Nagy, M. El-Beltagy, M. Wafa
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Improving p-value approximation and level accuracy of Monte Carlo tests by quasi-Monte Carlo methods
Communications in statistics. Simulation and computation, 2019We argue and show empirically that for the Monte Carlo test, if the pseudo-random numbers are replaced by a randomized low discrepancy sequence, the actual errors in approximating the p-value are smaller and the deviations of the exact level from the ...
S. Chiu, Kwong-Ip Liu
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Computer Physics Communications
Numerical simulations of models and theories that describe complex systems such as spin glasses are becoming increasingly important. Beyond fundamental research, these computational methods also find practical applications in fields like combinatorial ...
M. Bernaschi +8 more
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Numerical simulations of models and theories that describe complex systems such as spin glasses are becoming increasingly important. Beyond fundamental research, these computational methods also find practical applications in fields like combinatorial ...
M. Bernaschi +8 more
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arXiv.org
The Fokker-Planck (FP) particle method accelerates rarefied-gas simulations by replacing the binary collisions of the commonly used Direct Simulation Monte Carlo (DSMC) method with a drift=diffusion process.
Lukas Netterdon +3 more
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The Fokker-Planck (FP) particle method accelerates rarefied-gas simulations by replacing the binary collisions of the commonly used Direct Simulation Monte Carlo (DSMC) method with a drift=diffusion process.
Lukas Netterdon +3 more
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Genetic algorithms, pseudo-random numbers generators, and Markov chain Monte Carlo methods
2001presented at the Conference SCO99, 1999 ...
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Randomization and Monte Carlo Methods in Biology.
, 1990J. Gentle, B. Manly
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