Dear Dr. Penny,
thank you very much for your help.
I already tried to run the algorithm a couple of times using the same
configurations and I indeed noticed the variability due to the Gibbs
sampling. However there is still a clear difference between exceedance
probabilities when defining families as compared to when not.
I tried to increase the number of samples (as you suggested). However
I only find that option in the BMS:Maps GUI and not in the BMS:DCM
GUI, which I am using for model comparison (is that wrong?).
So do I have to increase the number of samples directly within the
Matlab-functions?
Thanks again for your help. Its great to get feedback when you're
stuck with a question concerning SPM/DCM.
Best wishes
Stefan
Quoting "Penny, William" <[log in to unmask]>:
> Dear Stefan,
>
> The computation of the exceedance probability is based on a sampling
> approach - which can potentially produce small differences each time
> you run it.
>
> To see if the differences are due to this 'sampling variability'
> perhaps you could just run
> the algorithm twice with exactly the same configuration (eg. both
> with no families) to see what the variability is.
>
> To reduce this variability you can increase the number of samples
> used - there's an option in the GUI for this.
>
> Best,
>
> Will.
>
>> -----Original Message-----
>> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
>> On Behalf Of Stefan Frässle
>> Sent: 13 June 2013 14:17
>> To: [log in to unmask]
>> Subject: [SPM] DCM: BMS
>>
>> Dear DCM experts,
>>
>> I have a question regarding BMS RFX for DCM. I compared 16 DCM models
>> for fMRI data and found one model to be the best with an exceedance
>> probability of around 0.6.
>>
>> In a subsequent analysis step I intended to compared the 16 models
>> according to a specific property, thus creating 4 different families
>> (each including 4 models) in order to compare those families.
>>
>> When running the batch (including the definition of the families) SPM
>> still shows me information about the comparison of the individual
>> models and I noticed that the exceedance probability for my winning
>> model is now around 0.7!
>>
>> I'm confused why there are differences in the exceedance probabilities
>> when defining families as compared to when I just compare the
>> individual models?
>>
>> I would be very grateful, if anyone could give his opinion on why this
>> occurs.
>>
>> Thanks a lot in advance
>> Stefan
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