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2018
Stochastic-simulation, or Monte-Carlo, methods are used extensively in the area of credit-risk modelling. This technique has, in fact, been employed inveterately in previous chapters. Care and caution are always advisable when employing a complex numerical technique. Prudence is particularly appropriate, in this context, because default is a rare event.
Philip Dutré +2 more
openaire +2 more sources
Stochastic-simulation, or Monte-Carlo, methods are used extensively in the area of credit-risk modelling. This technique has, in fact, been employed inveterately in previous chapters. Care and caution are always advisable when employing a complex numerical technique. Prudence is particularly appropriate, in this context, because default is a rare event.
Philip Dutré +2 more
openaire +2 more sources
2000
The written history of the Monte Carlo methods began in 1949 after the paper of Metropolis and Ulm [1949], but at that time the method had already been used for several years in secret defense projects of the United States of America for simulating the behaviour of nuclear reactors. It was definitely known to J. Von Neumann, N. Metropolis, S.M. Ulm, H.
Helmut Neunzert, Abul Hasan Siddiqi
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The written history of the Monte Carlo methods began in 1949 after the paper of Metropolis and Ulm [1949], but at that time the method had already been used for several years in secret defense projects of the United States of America for simulating the behaviour of nuclear reactors. It was definitely known to J. Von Neumann, N. Metropolis, S.M. Ulm, H.
Helmut Neunzert, Abul Hasan Siddiqi
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2009
Monte Carlo methods comprise a large and still growing collection of methods of repetitive simulation designed to obtain approximate solutions of various problems by playing games of chance. Often these methods are motivated by randomness inherent in the problem being studied (as, e.g., when simulating the random walks of “particles” undergoing ...
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Monte Carlo methods comprise a large and still growing collection of methods of repetitive simulation designed to obtain approximate solutions of various problems by playing games of chance. Often these methods are motivated by randomness inherent in the problem being studied (as, e.g., when simulating the random walks of “particles” undergoing ...
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2006
Abstract Starting with this chapter, we embark on a journey into the fascinating realms of statistical mechanics and computational physics. We set out to study a host of classical and quantum problems, all of value as models and with numerous applications and generalizations.
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Abstract Starting with this chapter, we embark on a journey into the fascinating realms of statistical mechanics and computational physics. We set out to study a host of classical and quantum problems, all of value as models and with numerous applications and generalizations.
openaire +1 more source
Progress on stochastic analytic continuation of quantum Monte Carlo data
Physics Reports, 2023Anders W Sandvik
exaly