Pavel: You are answering a different question, and the one I did not ask. So let me try again. If you compare equation (4) in Lunin et al.(2002) (or equation (14) in Lunin&Skovoroda,(1995)) and equation (14) in Murshudov et al.(1997) (or equation(1) in Cowtan,(2005)), you will see that the beta replaces the combination of the model error and experimental error. An assumption that beta is identical for every reflection in a given resolution shell is absolutely necessary for the minimization with respect to alpha/beta as outlined in appendix A of Lunin&Skovoroda. Derivation of the analytical expression breaks down if you introduce experimental errors for individual reflections. So as far as I am concerned, it appears that experimental errors are simply neglected. There must be a justification for this. It was my understanding that this is based on the assumption that the model errors overwhelm experimental errors, which is supposedly confirmed by showing that sqrt(beta)>>sigf (which is a different story with its own problems). So, here is my point. ML formalism as implemented in phenix does not "lump" experimental errors into some combined value. It simply ignores them because it assumes that they are much smaller than model errors. It is an important distinction. Cheers, Ed. On Mon, 2012-12-10 at 13:08 -0800, Pavel Afonine wrote:
Hi Ed,
I had always assumed that experimental sigmas were somehow lumped into the alpha and beta parameters (esp. given your discussion in section 2.3). In principle they could be, right?
Yes, they could be. I would like clarification on this. (...)
Alpha/beta are calculated in individual resolution shells. How can you "lump" experimental variance that varies by two orders of magnitude for individual reflections into a single parameter?
I'm sorry for being unclear (often email happens to be a poor way to express what you actually mean, unless you have heaps of time to write it lawyer style. There is always a room for reader to imagine unsaid. Sometimes it works, sometimes it doesn't.).
Anyways, extending my phrase above to better reflect what I actually meant: "Yes, they could be, to some extent (approximation). Surely, a proper way is to have sigmas in the formula explicitly, as I stated in previous email of that thread. Whether that makes any practically useful difference is debatable, and yet to answer."
I also wonder if introducing experimental uncertainty into Lunin's ML target is even possible without fundamentally altering it.
It is possible, and depends how fundamentally you want to alter it.
All the best, Pavel _______________________________________________ phenixbb mailing list [email protected] http://phenix-online.org/mailman/listinfo/phenixbb
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