Abstract
Due to the reason that the randomness of the parameters in the MASK algorithm always leads to the volatility and uncertainty of the mining results, this paper proposed an optimization algorithm for the maximum likelihood estima- tion of the parameters to choose a parameter that is most approximate to the common parameters from the parameter group that has been generated randomly. Such a parameter generated as above represented all of the parameters in the pa- rameter group. The simulation experiment proves that the application of such a parameter has reduced the great volatility hidden in the mining results to some extent.
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