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Reliability Engineering & System Safety, 2010
To reduce cost of Monte Carlo (MC) simulations for time-consuming processes, Bayesian Monte Carlo (BMC) is introduced in this paper. The BMC method reduces number of realizations in MC according to the desired accuracy level. BMC also provides a possibility of considering more priors. In other words, different priors can be integrated into one model by
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To reduce cost of Monte Carlo (MC) simulations for time-consuming processes, Bayesian Monte Carlo (BMC) is introduced in this paper. The BMC method reduces number of realizations in MC according to the desired accuracy level. BMC also provides a possibility of considering more priors. In other words, different priors can be integrated into one model by
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Optimization by Monte Carlo Methods
2009It may be that the problem of optimization entails more time and effort by mankind than any other mathematical problem. For example, it permeates nearly all design and engineering projects.
Ronald W. Shonkwiler, Franklin Mendivil
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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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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 ...
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Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey
Wiley Interdisciplinary Reviews: Computational Statistics, 2021Jiaxin Zhang
exaly
Monte Carlo and Quasi-Monte Carlo Methods 2004
2006Invariance Principles with Logarithmic Averaging for Ergodic Simulations.- Technical Analysis Techniques versus Mathematical Models: Boundaries of Their Validity Domains.- Weak Approximation of Stopped Dffusions.- Approximation of Stochastic Programming Problems.- The Asymptotic Distribution of Quadratic Discrepancies.- Weighted Star Discrepancy of ...
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Monte Carlo and Quasi-Monte Carlo Methods 2010
2012This book represents the refereed proceedings of the Ninth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Warsaw (Poland) in August 2010. These biennial conferences are major events for Monte Carlo and the premiere event for quasi-Monte Carlo research.
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Monte Carlo and Quasi-Monte Carlo Methods 2008
2009This volume represents the refereed proceedings of the Eighth International C- ference on Monte Carlo and Quasi-Monte Carlo Methods in Scienti c Computing, which was held at the University of Montreal, from 6-11 July, 2008. It contains a limited selection of articles based on presentations made at the conference.
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