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The goodness of fit between observed and predicted seismograms for the two models is evaluated using the variance reduction (VR) (Kuge, 2003).
The achieved convergence was determined using the variance reduction (VR) (e.g., Kubo et al. 2002) when (i) the difference between the summations of the VR values from all events in the current and former iterations was less than 1% or (ii) the current VR value was larger than that of the previous iteration.
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In this paper, we propose a Monte Carlo valuation algorithm that uses the variance reduction technique.
This is how portfolio variance, using the variance covariance method, is done.
Using the global variance reduction methods in SuperMC, the average speed-up is 292 times for the calculation of inboard TF coils nuclear heating, and 91 times for the calculation of global flux map, compared with the analog run.
Royston [ 20] addresses this problem, and using his equations the required sample size is reduced due to the variance reduction that occurs when using regression.
In this work, the Monte Carlo simulation was performed with pseudorandom number sequences and the variance reduction technique used is antithetic variables4.
We use the three-component waveforms to calculate the variance reduction.
Secondly, this formula is used to develop a set of generic design principles/heuristics for maximising the variance reduction of process streams.
Nonetheless, we still use the similar greedy heuristic as SHSD in [ 25] in order to maximize the variance reduction metric in (5) over the set of observations.
In Sec. 2, we described the variance reduction method that we developed to speed up the calculation of the reflectance using Monte Carlo simulations.
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