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In particular, it would be of interest to know whether the inference methods based on an assumed CES distribution can achieve "good" performance even if the data follow a different and, in general, more involved CES model.
The simulations in this paper represent even more difficult problems for the inference methods.
In this section, we aimed to investigate the following question: how well the inference methods meet the requirements of microarray data?
As only one of the inference methods tested (SHB) explicitly accounts for recombination, it is not surprising that the results on the sets simulated with recombination were quite poor.
The following sections describe the inference methods in detail.
The results of all the inference methods for DREAM5 expression data are summarized in Table 5.
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This can reduce the recall of the inference method to miss the inferring genes in the interaction networks.
On the accepted species tree, the inference method affected the number of duplications inferred for 154 clusters but had no effect on the other 323 clusters.
As a result, we have found that the inference method of the proposed system is similar to the human expert.
These results apply to IF THEN rules that use evaluative expressions (e.g., small and very big) and the inference method called perception-based logical deduction.
Notice that the focus here is not on reducing the computational cost of the inference method, and thus, we allow ourselves to use resource-consuming algorithms.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com