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Every refinement method needs a strategy to modify the model and a
scoring function to guide the direction of the modifications. Both least
squares and maximum likelihood are scoring functions with least-squares
indeed being a special case of maximum likelihood. Refinement methods
are for instance: steepest descend, conjugated gradient, simulated
annealing, monte carlo and I bet there are a lot more.
Bart
Peter Adrian Meyer wrote:
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Hi all,
Anthony Duff wrote (regarding R-fac and R-free from mapfiles?)
2. CNS does a worse job of refining a structure in the late stages,
even
accounting for differences in default restraint weights. (I don't know
why
this would be so, with both using maximum likelihood... maybe the CNS
algorithms are inferior?)
This reminded me of a question I've been wondering about for a bit: Does
maximum likelihood refer to a scoring function (generate gradients to
optimize while refining), or both a scoring function and refinement
menthod? As far as I understand, it's the first (based on what I've seen
of poking around in the internals of programs that do ML refinement vs
other types of refinement). But least-squares is a special case of
maximum likelihood, and least-squares (again as far as I know) is both a
scoring function and refinement method.
Could somebody more knowledgable about maximum likelihood clear this up?
Thanks,
Pete
Pete Meyer
Fu Lab
BMCB grad student
Cornell University
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