Results 231 to 240 of about 2,159,110 (277)
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2002
In the examples presented thus far the sampling has been unbiased; i.e., the points at which the independent variable was evaluated were selected from the distribution function that directly describes the sampling problem. Although nature always plays unbiased Monte Carlo, the practitioner cannot always do so.
Stephen A. Dupree, Stanley K. Fraley
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In the examples presented thus far the sampling has been unbiased; i.e., the points at which the independent variable was evaluated were selected from the distribution function that directly describes the sampling problem. Although nature always plays unbiased Monte Carlo, the practitioner cannot always do so.
Stephen A. Dupree, Stanley K. Fraley
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2010
The evaluation of the expectation of a given function of a solution of an SDE with jumps provides via the Feynman-Kac formula, see Sect. 2.7, the solution of a partial integro differential equation. In many applications it is of major interest to obtain numerically these expectations, in particular in multi-dimensional settings.
Eckhard Platen, Nicola Bruti-Liberati
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The evaluation of the expectation of a given function of a solution of an SDE with jumps provides via the Feynman-Kac formula, see Sect. 2.7, the solution of a partial integro differential equation. In many applications it is of major interest to obtain numerically these expectations, in particular in multi-dimensional settings.
Eckhard Platen, Nicola Bruti-Liberati
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2004
This chapter develops methods for increasing the efficiency of Monte Carlo simulation by reducing the variance of simulation estimates. These methods draw on two broad strategies for reducing variance: taking advantage of tractable features of a model to adjust or correct simulation outputs, and reducing the variability in simulation inputs. We discuss
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This chapter develops methods for increasing the efficiency of Monte Carlo simulation by reducing the variance of simulation estimates. These methods draw on two broad strategies for reducing variance: taking advantage of tractable features of a model to adjust or correct simulation outputs, and reducing the variability in simulation inputs. We discuss
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1992
In this chapter we shall describe several methods which allow a reduction in the variance of functionals of weak approximations of Ito diffusions. One method changes the underlying probability measure by means of a Girsanov transformation, another uses general principles of Monte-Carlo integration. Unbiased estimators are also constructed.
Peter E. Kloeden, Eckhard Platen
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In this chapter we shall describe several methods which allow a reduction in the variance of functionals of weak approximations of Ito diffusions. One method changes the underlying probability measure by means of a Girsanov transformation, another uses general principles of Monte-Carlo integration. Unbiased estimators are also constructed.
Peter E. Kloeden, Eckhard Platen
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Variance Reduction for Simulated Diffusions
SIAM Journal on Applied Mathematics, 1994Summary: This article develops some variance reduction techniques for the Monte- Carlo integration of functionals of the solutions of Itô stochastic differential equations (sdes). The Monte-Carlo method for sdes offers a means of calculating solutions to certain types of parabolic partial differential equations and so has applications in various fields
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2018
Variance reduction technique plays a crucial role in Monte Carlo methods, as they can significantly reduce the uncertainty inherited in Monte Carlo. While in the previous chapter we already have seen some basic variance reduction methods, here we present more advanced approaches.
Denis Belomestny, John Schoenmakers
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Variance reduction technique plays a crucial role in Monte Carlo methods, as they can significantly reduce the uncertainty inherited in Monte Carlo. While in the previous chapter we already have seen some basic variance reduction methods, here we present more advanced approaches.
Denis Belomestny, John Schoenmakers
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IEEE Transactions on Neural Networks and Learning Systems, 2023
Xia Jiang, Xianlin Zeng, Jian Sun
exaly
Xia Jiang, Xianlin Zeng, Jian Sun
exaly