cf.csur: Compute reduction in contour-distance
In mludkov/mlOSP: Machine Learning and Regression Monte Carlo Algorithms for Optimal Stopping
cf.csur R Documentation
Compute reduction in contour-distance
Description
cSUR for Contour Finding based on Ludkovski (2018)
Usage
cf.csur(objMean, objSd, nugget)
Arguments
objMean
predicted mean response
objSd
posterior standard deviation of the response
nugget
the noise variance to compute the ALC factor
Details
compute the change in ZC = sd*(1-sqrt(nugget))/sqrt(nugget + sd^2)
Author(s)
Mike Ludkovski
References
Mike Ludkovski, Kriging Metamodels and Experimental Design for Bermudan Option Pricing
Journal of Computational Finance, 22(1), 37-77, 2018
See Also
[osp.seq.design]
mludkov/mlOSP documentation built on April 29, 2023, 7:56 p.m.
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| cf.csur | R Documentation |
Compute reduction in contour-distance
Description
cSUR for Contour Finding based on Ludkovski (2018)
Usage
cf.csur(objMean, objSd, nugget)
Arguments
objMean |
predicted mean response |
objSd |
posterior standard deviation of the response |
nugget |
the noise variance to compute the ALC factor |
Details
compute the change in ZC = sd*(1-sqrt(nugget))/sqrt(nugget + sd^2)
Author(s)
Mike Ludkovski
References
Mike Ludkovski, Kriging Metamodels and Experimental Design for Bermudan Option Pricing Journal of Computational Finance, 22(1), 37-77, 2018
See Also
[osp.seq.design]
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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