Sentence examples for mapping models for from inspiring English sources

The strength of appearance-based mapping models for mobile robots lies in their ability to represent the environment through high-level image features and to provide human-readable information.

Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) provided statistical evidence for 13 genomic regions harboring QTLs on all maize chromosomes, with the exception of chromosome 5 (Tables  3 and 4).

Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) were combined and revealed 13 genomic regions significantly associated with the target traits in a complementary way.

In the short term, we hope to make the resource presented here sustainable, to provide regular updates and to add further data on the thalassemias and HbE [Colah et al., 2010; Olivieri et al., 2008], as we assemble data and develop tailored mapping models for these disorders.

In conclusion, it was possible to construct mapping models for the EQ-5D index using demographic, disease stage and cardiac outcome measures for a group of cardiac patients that performed better in predicting the EQ-5D index for values above 0.4, and less well for values below that level, where the EQ-5D index was over-estimated.

This study aimed to use individual patient data to derive mapping models for the EQ-5D index in cardiac patients with different levels of disease severity by incorporating into these models multiple demographic factors and clinical cardiac measures commonly used when treating and studying these patients.

It is necessary to generate a mathematical ionospheric forecasting and mapping model for TEC based on physical ionospheric influencing parameters.

Backward mapping is used for generation of lookup tables, while forward mapping is presented as an alternative and more efficient mapping model for specific cases.

23 Therefore, future work might include validating the mapping model for different patient groups and assessing the performance of an independent sample.

We also consider the BYM model here and compare its performance with two models not considered by Lawson et al. (2000): a version of the BYM model that is more robust to outliers and hence may be better able to detect abrupt changes in the spatial pattern of risk, and a spatial mixture model proposed by Green and Richardson (2002) (see the section on Bayesian disease-mapping models for details).

However, an interpretation of the different RCs has only been presented in detail for mapping models of one and two variables.