Vanessa Jackson
MSc Biology
Maximum likelihood methods are commonly used to reconstruct ancestral
character states on a phylogeny in order to test hypotheses about
evolution and adaptation. My thesis project examines the accuracy
of two maximum likelihood variants used to estimate discrete characters
states on evolutionary trees. In the first method, termed "global",
the transition-rate parameters for the likelihood model are estimated
only once by maximizing over all states. In the second method, termed
"local", a node is fixed in a certain state and then the transition
rate parameters are estimated conditional upon that state. A custom
software program was written to perform reconstructions on a variety
of simulated phylogenies, as well as phylogenies of real biological
organisms, in order to compare the accuracy of these methods. Preliminary
results suggest that the global method performs better on marginal
reconstructions, while the local method may perform better for joint
reconstructions.
Courses taken outside of major
Mathematics - Computational Biology
(MAT5932)
Statistics - Computational Methods in
Statistics II (STA5107)
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