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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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Journal of the Operational Research Society, 1985
Estimating real-world parameter values by means of Monte-Carlo/stochastic simulation is usually accomplished by carrying out a number ‘n’ of computer runs, each using random numbers taken from a pseudo-random number generator. In order to improve the accuracy of the estimate (reduce the estimate's variance), the most common recourse is to increase n ...
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Estimating real-world parameter values by means of Monte-Carlo/stochastic simulation is usually accomplished by carrying out a number ‘n’ of computer runs, each using random numbers taken from a pseudo-random number generator. In order to improve the accuracy of the estimate (reduce the estimate's variance), the most common recourse is to increase n ...
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Statistics & Probability Letters, 1995
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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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Water Resources Research, 1985
This paper presents an algorithm for optimal data collection in random fields, the so‐called variance reduction analysis, which is an extension of kriging. The basis of variance reduction analysis is an information response function (i.e., the amount of information gain at an arbitrary point due to a measurement at another site).
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This paper presents an algorithm for optimal data collection in random fields, the so‐called variance reduction analysis, which is an extension of kriging. The basis of variance reduction analysis is an information response function (i.e., the amount of information gain at an arbitrary point due to a measurement at another site).
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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
openaire +1 more source