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How does the number of partitions influence the number of inferred reticulations?
It is obvious that with LikelihoodType.BEST, network inference on an unpartitioned MSA will always lead to a tree.
However, it would be interesting to see how well NetRAX can recover reticulations in an unpartitioned MSA with LikelihoodType.AVERAGE... For this, I suggest an additional experiment where NetRAX is once run with and once without the partitions file.
The text was updated successfully, but these errors were encountered:
On 06.12.20 03:37, Sarah Lutteropp wrote:
How does the number of partitions influence the number of inferred
reticulations?
It is obvious that with LikelihoodType.BEST, network inference on an
unpartitioned MSA will always lead to a tree.
However, it would be interesting to see how well NetRAX can recover
reticulations in an unpartitioned MSA with LikelihoodType.AVERAGE...
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Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical Studies
Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
www.exelixis-lab.org
How does the number of partitions influence the number of inferred reticulations?
It is obvious that with LikelihoodType.BEST, network inference on an unpartitioned MSA will always lead to a tree.
However, it would be interesting to see how well NetRAX can recover reticulations in an unpartitioned MSA with LikelihoodType.AVERAGE... For this, I suggest an additional experiment where NetRAX is once run with and once without the partitions file.
The text was updated successfully, but these errors were encountered: