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In their application to hand written recognition, HMMs were utilized by observing lines and curves drawn on a 2D plane and inferring the most probable characters they represent [ 7].
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The most probable character history for each character was affected differently by the prior.
In contrast, the faster evolving character showed different most probable character histories depending on the prior.
The different prior values had a contrasting influence on the identification of the most probable character history.
For fast evolving characters, the levels of homoplasy will always be high and thus many equally most probable character histories will be found.
Our studies were limited to the southeast corner of the Saba Bank, but the results offer some insights on the probable character of gorgonian habitats throughout the Bank.
However, for the pollen unit, different values of E(T) always return different most probable character histories (Tables 2 and 3, Fig. 5).
It is important to stress that this concerns the most probable character history, a result not immediately available when using SIMMAP.
On the other hand, for the pollen unit, different character histories were most probable between the different values of E(T) as well as within the same analysis (several sub-equally probable character histories, Tables 2 and 3).
The differences were obvious in the two results reported here: the average number of transformations of character states (Table 1) and the most probable character history (Tables 2 and 3).
Although we have shown that the prior distribution on the transformation rate will affect the average number of transformations, we have also shown that it had a contrasting influence when identifying the most probable character history (Tables 2 and 3).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com