Results 221 to 230 of about 114,035 (266)

Monte-Carlo Simulation Adjusting

Proceedings of the AAAI Conference on Artificial Intelligence, 2014
In this paper, we propose a new learning method sim- ulation adjusting that adjusts simulation policy to im- prove the move decisions of the Monte Carlo method. We demonstrated simulation adjusting for 4 × 4 board Go problems. We observed that the rate of correct an- swers moderately increased.
Nobuo Araki, Masakazu Muramatsu, Kunihito Hoki, Satoshi Takahashi   +3 more
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Monte Carlo simulation for IRRMA

Applied Radiation and Isotopes, 2000
Monte Carlo simulation is fast becoming a standard approach for many radiation applications that were previously treated almost entirely by experimental techniques. This is certainly true for Industrial Radiation and Radioisotope Measurement Applications--IRRMA.
R P, Gardner, L, Liu
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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 simulation on microcomputers

SIMULATION, 1992
Monte Carlo analysis is a practical tech nique for including the effects of uncertainty in a model intended for decision support. It has been infrequently used, however, perhaps because it has been tedious and boring to do. A recently available software add-in to 1-2-3 named @Risk makes Monte Carlo analysis much easier.
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Green Simulation with Database Monte Carlo

ACM Transactions on Modeling and Computer Simulation, 2016
In a setting in which experiments are performed repeatedly with the same simulation model, green simulation means reusing outputs from previous experiments to answer the question currently being asked of the model. In this article, we address the setting in which experiments are run to answer questions quickly, with a time limit providing a fixed ...
Mingbin Feng, Jeremy Staum
openaire   +1 more source

Monte Carlo Simulation Method

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, Miguel Botto-Tobar   +1 more
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Overrelaxation and Monte Carlo simulation

Physical Review D, 1987
I study a simple variation of the algorithm of Metropolis et al. for simulating statistical systems. The trial changes in any given variable are taken from a region of phase space far from the old value but involving only small changes in energy. This results in correlation times which are short compared to the usual applications of the algorithm of ...
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Multicanonical Monte Carlo Simulations

International Journal of Modern Physics C, 1993
The 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 simulation in Fourier space

Computer Physics Communications, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Bayesian post-processing of Monte Carlo simulation in reliability analysis

Reliability Engineering and System Safety, 2022
Iason Papaioannou, Daniel Straub, Wolfgang Betz   +2 more
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