There is no need for any further comparisons since you have already done the right thing, i.e. applying BMS to the entire space of models considered. You can now simply report which model was optimal, state its expected posterior probability (or its exceedance probability) in relation to the other models, and report its parameter estimates.
Generally, note that selective comparisons of subsets of models are not equivalent to a proper model space partitioning approach unless you are using the agglomerative property of the Dirichlet distribution. As explained in the discussion of our BMS paper, the posterior belief about which model is most likely to have generated the data is a function of the entire set of models considered. This means that
changing model space (by reducing or extending the number of models considered) can change one's inference about the optimal model. As a consequence, one should always infer the most likely model by comparing the entire set of plausible models at once.
Von: Matthias Schurz <[log in to unmask]>
An: [log in to unmask]
Gesendet: Dienstag, den 14. Juli 2009, 15:48:22 Uhr
Betreff: Re: [SPM] DCM BMS results interpretation
Dear Dr. Stephan,
I'm glad to hear that the model space partitioning
approach will be further
developed. At the moment, i would like to write up the study of which i was
showing the results - with 16 models better than 48. Until the new method is
available: do you think a simple comparison of a single model out of the
best 16 (the worst among them) against a single model out of the worst 48
(the best among them) would also show that those 16 models are superior to
Please see the .pdf file for clarification.