[phenixbb] How to reduce clashscore value
Tim Fenn
tim.fenn at gmail.com
Sun Nov 20 20:00:36 PST 2011
On Sat, Nov 19, 2011 at 11:20 PM, Pavel Afonine <pafonine at lbl.gov> wrote:
> **
>
> The method of using the ratio of gradients doesn't make sense in a
> maximum likelihood context,
>
>
> assuming that by "a maximum likelihood context" you mean refinement using
> a maximum-likelihood (ML) criterion as X-ray term (or, more generally, I
> would call it experimental data term, as it can be neutron too, for
> instance), I find the whole statement above as a little bit strange since
> it mixes different and absolutely not related things: type of
> crystallographic data term and a method of relative scale (weight)
> determination between it and the other term (restraints).
>
> I don't see how the choice of crystallographic data term (LS, ML,
> real-space or any other) is related to the method of this scale
> determination.
>
>
This shouldn't be a surprise - in short, the errors are used as weights in
LS and ML optimization targets, the latter just uses a different form for
the errors that estimates all the model and unmeasured uncertainties (like
phase error). So if the data is poorly predicted by a model, the ML target
is broader/flatter (as are the gradients!), while good/complete models will
yield a sharper ML target. So the likelihood target is naturally weighted,
in a sense. This doesn't happen with least squares (unless the weights are
not the inverse variances, which seems to be what the MLMF paper you
mentioned is doing?).
The likelihood function can then be plugged in to Bayes' law - if the model
and data error terms are all accounted for, no other weighting should be
necessary. This is discussed in Airlie McCoy's excellent review (
http://dx.doi.org/10.1107/S0907444904016038) - see sections 4.4 and 4.6,
and the derivation is also in http://dx.doi.org/10.1107/S0907444911039060
Hope this helps!
Regards,
Tim
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