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Principal component analysis (PCA) based pattern matching methods have been applied to process monitoring and fault detection.

Probability matching methods have been used to link together individual hospital records for each patient, thereby creating "linked" patient histories.

Propensity score matching methods have been found to account well for measured confounding.

In attempts to overcome the above limitations, various matching methods have been developed that use the features extracted from multiple sequence alignment (MSA) of the protein family sequences.

Propensity score matching methods have been found to account well for measured confounding 12 and using propensity score matching enabled us to eliminate differences between public and private patients for the maternal characteristics where information was available.

Several matching methods have been proposed in the literature, the most widely used being the Nearest-Neighbor matching, the Radius matching, and the Kernel matching methods.

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To recognize the relationships in the literatures, the pattern matching method has also been proposed for extracting information on protein-protein interaction from scientific literatures [12].

Within this context, the propensity score matching method has been used to reduce the bias induced by this incomplete matching 16.

From Fig. 9d, f, the disparity maps generated by the cross-based local stereo matching method have many inaccurate areas in foreground objects; inaccurate areas are often produced by the cost aggregation step of the local-based method in the smooth areas of the color image.

For networks with few unknown parameters, these 'simulate-and-match' methods have been successful at finding good values, or multiple good candidates.

More generally, this study has found that the minimax method and prevalence matching methods give very similar results.

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