Thanks so much for the clarification. Since I'm somewhat new to MCMC algorithms, tour explanation will really help me explain it to others.
Peggy On Thu, Dec 18, 2014 at 6:30 PM, Anastasia Yendiki < [email protected]> wrote: > > > Hi Peggy - The reinit option is meant to use when you don't have a > valid-appearing tract the first time (you have a tract that looks like a > single line, which means that the first initialization of the path was > poor, i.e., outside the white matter or something like that). If you have a > valid-appearing tract with the first initialization, but not with another > initialization, then the first one is the one to keep. If you have a case > where you have different results but you are unsure which one is valid, I > can look at it if you upload here: > https://gate.nmr.mgh.harvard.edu/filedrop2/ > > The nsample is the number of path samples that are saved and added up to > give you the final path distribution. In principle, if you found that you > were not converging reliably to the same solution with this number of > samples (with a valid initialization), you would increase the number. > > The number of burn-in samples is a number of samples that are run in the > very beginning and discarded, before the nsample samples that are actually > saved. As long as the algorithm starts from a resonable initial guess, this > shouldn't have much of an effect. The nkeep parameter means that 1 sample > path is saved for every nkeep sample paths that are drawn. Not keeping > every sample increases independence between samples. These are standard > aspects of an MCMC algorithm. > > a.y > > On Mon, 15 Dec 2014, Peggy Skelly wrote: > > I've been interested in these 2 questions, since I have similar >> issues:https://www.mail-archive.com/freesurfer@nmr. >> mgh.harvard.edu/msg38213.html >> http://www.mail-archive.com/[email protected]/msg39282.html >> >> What does it mean if I find a valid-appearing tract with reinit=0, but >> then lose it with reinit=1? >> >> How much variability should I expect in the path stats between runs using >> the same parameters? >> I haven't gotten as far as the previous question to see if statistical >> significance changes, but since I'm looking at longitudinal data, I >> expect the actual white matter changes over time to be rather small. >> >> How do the dmrirc parameters nburnin, nsample, and nkeep affect the MCMC >> process and results? >> Since the built in randomness should decrease the number of samples is >> increase, should I just increase nsample? >> >> Thanks, >> Peggy >> >> >> >> > _______________________________________________ > Freesurfer mailing list > [email protected] > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > The information in this e-mail is intended only for the person to whom it > is > addressed. If you believe this e-mail was sent to you in error and the > e-mail > contains patient information, please contact the Partners Compliance > HelpLine at > http://www.partners.org/complianceline . If the e-mail was sent to you in > error > but does not contain patient information, please contact the sender and > properly > dispose of the e-mail. > >
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