Dear Vladimir,
Thank you for your reply.
Yes of course it is not easy or maybe even practical to debug this program in PCT but I thought there might be a simple explanation for that.
Best,
Pegah
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Vladimir Litvak
Sent: 29 August 2012 15:16
To: [log in to unmask]
Subject: Re: [SPM] DCM/EEG BMS results
Dear Pegah,
DCM.F itself is approximation to the LOG evidence so a difference of
256 in F is a very large difference. A difference of 3 is sufficient to get one of the models 95% probable (in the two model case).
Regarding the source of the differences as I learned being a computer science undergraduate:
1) Every bug has a reason.
2) If a bug seems to have no reason, see (1).
3) It is always worth the effort to investigate this kind of problems as they almost always originate from some more serious issues.
However, I cannot tell you without going through the code line by line in the debugger what is happening and in the parallel case it can be quite tricky.
Best,
Vladimir
On Wed, Aug 29, 2012 at 2:52 PM, Tayaranian Hosseini P.
<[log in to unmask]> wrote:
> Dear Vladimir,
>
>
>
> I again checked the two models and the priors are exactly the same. I
> checked all the parameters and matrices in DCM.M and DCM.M.dipfit and
> they are the same but all the amounts in posterior parameters such as
> DCM.Ep, DCM.Cg, DCM.Cp,DCM.Eg, etc are different. And when I check the
> mode predictions for the two models, the first one (with higher BMS)
> predicts the original modes much better than the second model.
>
>
>
> But I still don't get it why when the two models are exactly the same
> and they are applied on the same dataset, they should give different results?
> Also, when DCM.F values are very close, why should I see such big
> difference between the two models such that it will select one over
> the other? Are the log-evidence values that are plotted using BMS the
> same as log10(DCM.F) or
> log(DCM.F) or any other parameter affects this comparison?
>
>
>
> Best,
>
> Pegah
>
>
>
>
>
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
> On Behalf Of Vladimir Litvak
> Sent: 29 August 2012 14:04
> To: [log in to unmask]
> Subject: Re: [SPM] DCM/EEG BMS results
>
>
>
> Dear Pegah,
>
>
>
> That sounds strange and my guess would be that you also have slightly
> different SPM or Matlab versions in the two cases and that's what
> makes it different. You could try comparing all the posteriors, like
> spm_vec(DCM.Ep) and spm_vec(DCM.Cp) and look for differences.
>
>
>
> Vladimir
>
>
>
> On 29 Aug 2012, at 13:27, Tayaranian Hosseini P. wrote:
>
>
>
> Hello,
>
>
>
> I have tried a specific DCM model on different EEG datasets. Then, to
> speed up the calculations I tried parallel computing toolbox (PCT) of
> MATLAB and applied the same model on the same datasets but when I
> apply BMS (fixed
> effects) on the results of the two models (before and after PCT) for
> each dataset separately, the one before PCT always gives me higher
> probability than the second one whereas I think they should be the
> same because I have not changed any parameter in the model. Also, for
> each dataset, F is similar for the two models for example 67632 vs
> 67376 in one of the datasets. I checked the IDs for each set and they
> were the same for the two conditions (before and after PCT).
>
>
>
> What else should I check in the DCM result to find out where this
> change comes from?
>
>
>
> Best,
>
> Pegah
>
>
>
> --------------------------------------------------------
>
> Pegah Tayaranian Hosseini
>
> PhD Student
>
> Room 4077, Tizard building (13)
>
> Institute of Sound and Vibration Research
>
> University of Southampton, SO17 1BJ, UK
>
>
>
> Tel: 023 8059 2850
>
> email: [log in to unmask]
>
>
>
>
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